diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 02d31b7..7dac0d9 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,10 +9,6 @@ repos:
     -   id: check-yaml
     -   id: check-added-large-files
         args: ['--maxkb=4096']
--   repo: https://github.com/shellcheck-py/shellcheck-py
-    rev: v0.10.0.1
-    hooks:
-    -   id: shellcheck
 -   repo: https://github.com/astral-sh/ruff-pre-commit
     rev: v0.12.2
     hooks:
diff --git a/Cargo.lock b/Cargo.lock
index 9c651b9..3d39a13 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -258,7 +258,7 @@ checksum = "0fda2ff0d084019ba4d7c6f371c95d8fd75ce3524c3cb8fb653a3023f6323e64"
 
 [[package]]
 name = "stringalign"
-version = "0.1.3"
+version = "0.1.4"
 dependencies = [
  "numpy",
  "pyo3",
diff --git a/examples/compare_transcription_eval_tools/README.md b/examples/compare_transcription_eval_tools/README.md
new file mode 100644
index 0000000..2f14d0c
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/README.md
@@ -0,0 +1,9 @@
+# Code for comparing the WER and CER across tools
+
+This analysis is inspired by [https://dl.acm.org/doi/epdf/10.1145/3476887.3476888](https://dl.acm.org/doi/epdf/10.1145/3476887.3476888). Use the following code to run it.
+
+```
+bash synthetic_emoji_run_analysis.sh && bash hip21_run_analysis.sh
+```
+
+Note that running the analysis requires pipx and Docker are installed on your system.
diff --git a/examples/compare_transcription_eval_tools/calamari/evaluate.py b/examples/compare_transcription_eval_tools/calamari/evaluate.py
new file mode 100644
index 0000000..e361619
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/calamari/evaluate.py
@@ -0,0 +1,835 @@
+"""
+Modified from calamari: https://github.com/Calamari-OCR/calamari
+
+Copyright 2020 The tfaip Authors.  All rights reserved.
+
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+
+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU Affero General Public License into a single
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+License will continue to apply to the part which is the covered work,
+but the special requirements of the GNU Affero General Public License,
+section 13, concerning interaction through a network will apply to the
+combination as such.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU General Public License from time to time.  Such new versions will
+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU General
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+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
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+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
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+to choose that version for the Program.
+
+  Later license versions may give you additional or different
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+  15. Disclaimer of Warranty.
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+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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+SUCH DAMAGES.
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+
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+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
+"""
+
+import logging
+from collections import namedtuple
+
+from edit_distance import edit_distance
+from textprocessors import synchronize
+
+logger = logging.getLogger(__name__)
+SingleEvalData = namedtuple("SingleEvalData", ["chars", "char_errs", "sync_errs", "conf", "gt_pred"])
+
+
+class Evaluator:
+    @staticmethod
+    def evaluate_single_args(args):
+        return Evaluator.evaluate_single(**args)
+
+    @staticmethod
+    def evaluate_single(_sentinel=None, gt="", pred="", skip_empty_gt=False):
+        """Evaluate a single pair of data
+
+        Parameters
+        ----------
+        _sentinel : None
+            Sentinel to force to specify gt and pred manually
+        gt : str
+            ground truth
+        pred : str
+            prediction
+        skip_empty_gt : bool
+            skip gt text lines that are empty
+
+        Returns
+        -------
+        int
+            length of ground truth
+        int
+            number of errors
+        int
+            number of synchronisation errors
+        dict
+            confusions dictionary
+        tuple(str, str)
+            ground_truth, prediction (same as input)
+
+        """
+        confusion = {}
+        total_sync_errs = 0
+
+        if len(gt) == 0 and skip_empty_gt:
+            return 0, 0, 0, confusion, (gt, pred)
+
+        errs, trues = edit_distance(gt, pred)
+        synclist = synchronize([gt, pred])
+        for sync in synclist:
+            gt_str, pred_str = sync.get_text()
+            if gt_str != pred_str:
+                key = f"{gt_str!r} -> {pred_str!r}"
+                total_sync_errs += max(len(gt_str), len(pred_str))
+                if key not in confusion:
+                    confusion[key] = 1
+                else:
+                    confusion[key] += 1
+
+        return len(gt), errs, total_sync_errs, confusion, (gt, pred)
+
+    @staticmethod
+    def evaluate_single_list(eval_results, store_all=False):
+        # sum all errors up
+        all_eval = []
+        total_instances = 0
+        total_chars = 0
+        total_char_errs = 0
+        confusion = {}
+        total_sync_errs = 0
+        for chars, char_errs, sync_errs, conf, gt_pred in eval_results:
+            if store_all:
+                all_eval.append(SingleEvalData(chars, char_errs, sync_errs, conf, gt_pred)._asdict())
+
+            total_instances += 1
+            total_chars += chars
+            total_char_errs += char_errs
+            total_sync_errs += sync_errs
+            for key, value in conf.items():
+                if key not in confusion:
+                    confusion[key] = value
+                else:
+                    confusion[key] += value
+
+        # Note the sync errs can be higher than the true edit distance because
+        # replacements are counted as 1
+        # e.g. ed(in ewych, ierg ch) = 5
+        #      sync(in ewych, ierg ch) = [{i: i}, {n: erg}, {ewy: }, {ch: ch}] = 6
+
+        return {
+            "single": all_eval,
+            "total_instances": total_instances,
+            "avg_ler": total_char_errs / total_chars,
+            "total_chars": total_chars,
+            "total_char_errs": total_char_errs,
+            "total_sync_errs": total_sync_errs,
+            "confusion": confusion,
+        }
+
+    def evaluate(self, *, gt_data: dict[str, str], pred_data: dict[str, str]):
+        """evaluate on the given raw data
+
+        Parameters
+        ----------
+        gt_data : Dataset, optional
+            the ground truth
+        pred_data : Dataset
+            the prediction dataset
+
+        Returns
+        -------
+        evaluation dictionary
+        """
+        non_existing_pred_handling_mode = "empty"  # This is the default
+        if non_existing_pred_handling_mode != "error":
+            n_empty = 0
+            mapped_pred_data = {}
+            for sample_id in list(gt_data.keys()):
+                if sample_id in pred_data:
+                    mapped_pred_data[sample_id] = pred_data[sample_id]
+                else:
+                    if non_existing_pred_handling_mode == "empty":
+                        mapped_pred_data[sample_id] = ""
+                    else:
+                        del gt_data[sample_id]  # skip
+                    n_empty += 1
+            logger.info(f"{n_empty}/{len(gt_data)} lines could not be matched during the evaluation.")
+            if n_empty == len(gt_data):
+                raise ValueError(
+                    f"No lines could be matched by their ID. First 10 gt ids "
+                    f"{list(gt_data.keys())[:10]}, first 10 pred ids {list(pred_data.keys())[:10]}"
+                )
+            pred_data = mapped_pred_data
+
+        gt_ids, pred_ids = set(gt_data.keys()), set(pred_data.keys())
+        if len(gt_ids) != len(gt_data):
+            raise ValueError("Non unique keys in ground truth data.")
+        if gt_ids != pred_ids:
+            raise Exception(
+                f"Mismatch in gt and pred. Samples could not be matched by ID. "
+                f"GT without PRED: {gt_ids.difference(pred_ids)}. "
+                f"PRED without GT: {pred_ids.difference(gt_ids)}"
+            )
+
+        gt_pred = [(gt_data[s_id], pred_data[s_id]) for s_id in gt_ids]
+
+        out = [Evaluator.evaluate_single_args({"gt": gt, "pred": pred, "skip_empty_gt": False}) for gt, pred in gt_pred]
+
+        res = Evaluator.evaluate_single_list(out, True)
+        res["ids"] = list(gt_ids)
+        return res
diff --git a/examples/compare_transcription_eval_tools/calamari/run_calamari.Dockerfile b/examples/compare_transcription_eval_tools/calamari/run_calamari.Dockerfile
new file mode 100644
index 0000000..24ad5d5
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/calamari/run_calamari.Dockerfile
@@ -0,0 +1,9 @@
+FROM python:3.14-trixie
+
+RUN pip install edit_distance==1.0.7 numpy==2.4.2
+
+WORKDIR /analysis
+COPY ocr_results/ ./ocr_results/
+COPY evaluate.py textprocessors.py run_calamari.py .
+
+CMD ["python", "run_calamari.py"]
diff --git a/examples/compare_transcription_eval_tools/calamari/run_calamari.py b/examples/compare_transcription_eval_tools/calamari/run_calamari.py
new file mode 100644
index 0000000..ad0ede9
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/calamari/run_calamari.py
@@ -0,0 +1,30 @@
+import json
+import pathlib
+
+import evaluate
+
+input_dir = pathlib.Path("ocr_results")
+out_dir = pathlib.Path("output")
+
+char_report_files = []
+word_report_files = []
+
+all_ref = {}
+all_pred = {}
+
+result = {}
+for i, ref_file in enumerate(input_dir.glob("*.ref.txt")):
+    print(f"Analysing file {i}")
+    name = ref_file.name.partition(".")[0]
+    ref = {name: (input_dir / f"{name}.ref.txt").read_text(encoding="utf-8")}
+    pred = {name: (input_dir / f"{name}.pred.txt").read_text(encoding="utf-8")}
+
+    all_ref |= ref
+    all_pred |= pred
+
+    result[name] = evaluate.Evaluator().evaluate(gt_data=ref, pred_data=pred)
+
+result["overall"] = evaluate.Evaluator().evaluate(gt_data=all_ref, pred_data=all_pred)
+
+with open(out_dir / "result.json", "w") as f:
+    json.dump(result, f, indent=2)
diff --git a/examples/compare_transcription_eval_tools/calamari/run_calamari.sh b/examples/compare_transcription_eval_tools/calamari/run_calamari.sh
new file mode 100644
index 0000000..f110259
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/calamari/run_calamari.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+cp -r ../ocr_results ./ocr_results/
+docker build -f run_calamari.Dockerfile -t stringalign_run_calamari .
+rm -rf ./ocr_results/
+mkdir -p calamari_results
+
+
+docker run --rm --volume $(pwd)/calamari_results:/analysis/output stringalign_run_calamari
+docker image rm stringalign_run_calamari
diff --git a/examples/compare_transcription_eval_tools/calamari/textprocessors.py b/examples/compare_transcription_eval_tools/calamari/textprocessors.py
new file mode 100644
index 0000000..dd56706
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/calamari/textprocessors.py
@@ -0,0 +1,831 @@
+"""
+
+Modified from calamari: https://github.com/Calamari-OCR/calamari
+
+Copyright 2020 The tfaip Authors.  All rights reserved.
+
+                 GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
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+  The licenses for most software and other practical works are designed
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+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
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+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
+"""
+
+import numpy as np
+
+
+class Sync:
+    def __init__(self, texts, substr=None, match=None):
+        self.texts = texts
+
+        if substr:
+            assert substr.shape[0] == len(self.texts)
+            self.substr = substr
+        else:
+            self.substr = np.zeros((len(texts), 3), dtype=int)
+
+        self.match = match
+
+    def __str__(self):
+        return str(self.substr)
+
+    def get_text(self):
+        return [self.texts[i][start : start + length] for i, (start, end, length) in enumerate(self.substr)]
+
+    def is_valid(self):
+        return np.any(self.substr[:, 2] > 0)
+
+    def lengths(self):
+        return self.substr[:, 2]
+
+    def start(self, idx):
+        return self.substr[idx, 0]
+
+    def stop(self, idx):
+        return self.substr[idx, 1]
+
+    def length(self, idx):
+        return self.substr[idx, 2]
+
+    def set_start(self, idx, v):
+        self.substr[idx, 0] = v
+
+    def set_stop(self, idx, v):
+        self.substr[idx, 1] = v
+
+    def set_length(self, idx, v):
+        self.substr[idx, 2] = v
+
+    def set_all(self, idx, v):
+        self.substr[idx, :] = v
+
+
+def synchronize(texts):
+    def init():
+        sync = Sync(texts)
+        for i, text in enumerate(texts):
+            sync.set_all(i, [0, len(text) - 1, len(text)])
+
+        if sync.is_valid():
+            return [sync]
+
+        return []
+
+    def longest_match(maxlen, c1, start1, stop1, c2, start2, stop2):
+        mstart1 = 0
+        mstart2 = 0
+        s1limit = stop1 - maxlen
+        s2limit = stop2 - maxlen
+        for s1 in range(start1, s1limit + 1):
+            for s2 in range(start2, s2limit + 1):
+                if c1[s1] == c2[s2]:
+                    i1 = s1 + 1
+                    i2 = s2 + 1
+                    while i1 <= stop1 and i2 <= stop2 and c1[i1] == c2[i2]:
+                        i1 += 1
+                        i2 += 1
+
+                    increase = i1 - s1 - maxlen
+                    if increase > 0:
+                        s1limit -= increase
+                        s2limit -= increase
+                        maxlen += increase
+                        mstart1 = s1
+                        mstart2 = s2
+
+        return maxlen, mstart1, mstart2
+
+    def save_match(synclist, num_text, sync, start, length, match):
+        left, right = Sync(texts), Sync(texts)
+        for i in range(num_text):
+            stop = start[i] + length - 1
+            left.set_all(i, [sync.start(i), start[i] - 1, start[i] - sync.start(i)])
+            right.set_all(i, [stop + 1, sync.stop(i), sync.stop(i) - stop])
+            sync.set_all(i, [start[i], stop, length])
+
+        sync.match = match
+        if left.is_valid():
+            synclist.insert(synclist.index(sync), left)
+
+        if right.is_valid():
+            synclist.insert(synclist.index(sync) + 1, right)
+
+    def recursive_sync(synclist, texts, start_index):
+        sync = synclist[start_index]
+        if np.any(sync.lengths() == 0):
+            return
+
+        start = np.zeros(len(texts), dtype=int)
+        start[0] = sync.start(0)
+        length = sync.length(0)
+        for i, text in enumerate(texts[1:], 1):
+            length, new_start, start[i] = longest_match(
+                0,
+                texts[0],
+                start[0],
+                start[0] + length - 1,
+                text,
+                sync.start(i),
+                sync.stop(i),
+            )
+
+            if length == 0:
+                return
+
+            change = new_start - start[0]
+            if change > 0:
+                for j in range(i):
+                    start[j] += change
+
+        save_match(synclist, len(texts), sync, start, length, True)
+
+        start_index = synclist.index(sync)
+        if start_index - 1 >= 0:
+            recursive_sync(synclist, texts, start_index - 1)
+
+        start_index = synclist.index(sync)
+        if start_index + 1 < len(synclist):
+            recursive_sync(synclist, texts, start_index + 1)
+
+        return
+
+    synclist = init()
+
+    if len(synclist) > 0:
+        recursive_sync(synclist, texts, 0)
+
+    return synclist
+
+
+if __name__ == "__main__":
+    synclist = synchronize(["AbcdEfG", "cdEFG"])
+    print([s.get_text() for s in synclist])
diff --git a/examples/compare_transcription_eval_tools/check_for_multi_codepoint_grapheme_clusters.py b/examples/compare_transcription_eval_tools/check_for_multi_codepoint_grapheme_clusters.py
new file mode 100644
index 0000000..964e047
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/check_for_multi_codepoint_grapheme_clusters.py
@@ -0,0 +1,15 @@
+# /// script
+# requires-python = ">=3.13"
+# dependencies = ["regex"]
+# ///
+
+import unicodedata
+from pathlib import Path
+
+import regex
+
+text = Path("data.txt").read_text()
+matches = regex.findall(r"\X", text)
+for match in matches:
+    if len(unicodedata.normalize("NFC", match)) > 1:
+        print(match)
diff --git a/examples/compare_transcription_eval_tools/dinglehopper/container_script.sh b/examples/compare_transcription_eval_tools/dinglehopper/container_script.sh
new file mode 100644
index 0000000..38c5c24
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/dinglehopper/container_script.sh
@@ -0,0 +1,11 @@
+mkdir pred ref && \
+    bash -c 'for f in ocr_results/*ref.txt; do cp "$f" "ref/$(basename "$f" .ref.txt).txt"; done' && \
+    bash -c 'for f in ocr_results/*pred.txt; do cp "$f" "pred/$(basename "$f" .pred.txt).txt"; done'
+
+
+dinglehopper ref/ \
+    pred/ \
+    report \
+    output/ \
+    --plain-encoding utf-8 && \
+    dinglehopper-summarize output/
diff --git a/examples/compare_transcription_eval_tools/dinglehopper/run_dinglehopper.Dockerfile b/examples/compare_transcription_eval_tools/dinglehopper/run_dinglehopper.Dockerfile
new file mode 100644
index 0000000..4ca27f4
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/dinglehopper/run_dinglehopper.Dockerfile
@@ -0,0 +1,11 @@
+FROM python:3.14-trixie
+
+RUN pip install dinglehopper==0.11.0
+
+WORKDIR /analysis
+COPY ocr_results/ ./ocr_results/
+RUN mkdir pred ref && \
+    bash -c 'for f in ocr_results/*ref.txt; do cp "$f" "ref/$(basename "$f" .ref.txt).txt"; done' && \
+    bash -c 'for f in ocr_results/*pred.txt; do cp "$f" "pred/$(basename "$f" .pred.txt).txt"; done'
+
+CMD ["bash", "-c", "dinglehopper ref/ pred/ report output/  --plain-encoding utf-8 && dinglehopper-summarize output/"]
diff --git a/examples/compare_transcription_eval_tools/dinglehopper/run_dinglehopper.sh b/examples/compare_transcription_eval_tools/dinglehopper/run_dinglehopper.sh
new file mode 100644
index 0000000..811f55e
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/dinglehopper/run_dinglehopper.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+cp -r ../ocr_results ./ocr_results/
+docker build -f run_dinglehopper.Dockerfile -t stringalign_run_dinglehopper .
+rm -rf ./ocr_results/
+mkdir -p dinglehopper_results
+
+
+docker run --rm --volume $(pwd)/dinglehopper_results:/analysis/output stringalign_run_dinglehopper
+docker image rm stringalign_run_dinglehopper
diff --git a/examples/compare_transcription_eval_tools/hip21_get_data.py b/examples/compare_transcription_eval_tools/hip21_get_data.py
new file mode 100644
index 0000000..0f2c2b0
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/hip21_get_data.py
@@ -0,0 +1,56 @@
+import csv
+import xml.etree.ElementTree as ET
+from collections.abc import Generator
+from pathlib import Path
+
+from tqdm import tqdm
+
+
+def get_page_text(file: Path) -> Generator[str]:
+    root = ET.parse(file)
+    ns = {"PAGE": "http://schema.primaresearch.org/PAGE/gts/pagecontent/2010-03-19"}
+    unicode_tags = root.findall(".//PAGE:TextRegion/PAGE:TextEquiv/PAGE:Unicode", ns)
+    for unicode_tag in unicode_tags:
+        if text := unicode_tag.text.strip():  # type: ignore
+            yield text + "\n"
+
+
+def get_alto_text(file: Path) -> Generator[str]:
+    root = ET.parse(file)
+    ns = "http://www.loc.gov/standards/alto/ns-v3#"
+    text_line_tags = root.findall(".//ALTO:TextLine", {"ALTO": ns})
+    for text_line_tag in text_line_tags:
+        out = []
+        for tag in text_line_tag:
+            tag_type = tag.tag.casefold()
+            if tag_type == f"{{{ns}}}string":
+                out.append(tag.attrib["CONTENT"])
+            elif tag_type == f"{{{ns}}}sp":
+                out.append(" ")
+            elif tag_type == f"{{{ns}}}hyp":
+                out.append(tag.attrib["CONTENT"])
+            else:
+                raise ValueError()
+
+        if text := "".join(out).strip():
+            yield text + "\n"
+
+
+repo_parent = Path(__file__).parent / "hip21_ocrevaluation"
+data_parent = repo_parent / "data"
+output_dir = Path(__file__).parent / "ocr_results"
+output_dir.mkdir(exist_ok=True)
+
+with open(repo_parent / "primaID.csv") as f:
+    reader = csv.DictReader(f)
+    stems = [row["\ufeffprimaID"] for row in reader if row["dataset"] == "impact"]
+
+for stem in tqdm(sorted(stems)):
+    gt_xml = data_parent / f"{stem}.gt.xml"
+    gt4hist_xml = data_parent / f"{stem}.gt4hist.xml"
+
+    with open(output_dir / f"{stem}.ref.txt", "w", encoding="utf-8") as f:
+        f.writelines(get_page_text(gt_xml))
+
+    with open(output_dir / f"{stem}.pred.txt", "w", encoding="utf-8") as f:
+        f.writelines(get_alto_text(gt4hist_xml))
diff --git a/examples/compare_transcription_eval_tools/hip21_get_results.py b/examples/compare_transcription_eval_tools/hip21_get_results.py
new file mode 100644
index 0000000..3b9aff1
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/hip21_get_results.py
@@ -0,0 +1,140 @@
+# /// script
+# requires-python = ">=3.13"
+# dependencies = ["pandas", "jinja2"]
+# ///
+"""
+This script gets the results from all the string comparison tools and creates a single DataFrame to display them
+"""
+
+import json
+import pathlib
+import re
+
+import pandas as pd
+
+pages = [f.stem.partition(".")[0] for f in pathlib.Path("ocr_results").iterdir()]
+
+results = {}
+
+
+# Load Calamari results
+with open("calamari/calamari_results/result.json", "r") as f:
+    calamari = json.load(f)
+results["Calamari"] = {key: {"cer": value["avg_ler"]} for key, value in calamari.items()}
+
+
+# Load Dinglehopper results
+dinglehopper_path = pathlib.Path("dinglehopper/dinglehopper_results")
+results["Dinglehopper"] = {}
+
+for page in pages:
+    with open(dinglehopper_path / f"{page}.txt-report.json", "r") as f:
+        page_data = json.load(f)
+
+    results["Dinglehopper"][page] = {"cer": page_data["cer"], "wer": page_data["wer"]}
+
+with open(dinglehopper_path / "summary.json", "r") as f:
+    summary_data = json.load(f)
+results["Dinglehopper"]["overall"] = {"cer": summary_data["cer_avg"], "wer": summary_data["wer_avg"]}
+
+# Load ISRI results
+isri_path = pathlib.Path("isri/isri_results")
+results["ISRI"] = {}
+
+
+def get_isri_error_rate(file: pathlib.Path) -> float:
+    with open(file, "r") as f:
+        report = f.readlines()
+
+    accuracy_line = report[4]
+    (accuracy_match,) = re.findall(r"(\d+(?:\.\d+)?)%\s+Accuracy", accuracy_line)
+    return 1 - float(accuracy_match) / 100
+
+
+results["ISRI"]["overall"] = {
+    "cer": get_isri_error_rate(isri_path / "character_report.txt"),
+    "wer": get_isri_error_rate(isri_path / "word_report.txt"),
+}
+
+for page in pages:
+    results["ISRI"][page] = {
+        "cer": get_isri_error_rate(isri_path / f"{page}.char_report.txt"),
+        "wer": get_isri_error_rate(isri_path / f"{page}.word_report.txt"),
+    }
+
+# Load jiwer
+with open("jiwer/jiwer_results/result.json", "r") as f:
+    results["Jiwer"] = json.load(f)
+
+# Load meeteval
+with open("meeteval/meeteval_results/result.json", "r") as f:
+    results["Meeteval"] = json.load(f)
+
+
+# Load ocrevalUAtion
+def parse_ocrevalUAtion_report(file: pathlib.Path) -> dict[str, float]:
+    with open(file, "r") as f:
+        report = f.readlines()
+    cer_line = report[10]
+    (cer,) = re.findall(r"<td>CER</td><td>(\d+(?:\.\d+)?)</td>", cer_line)
+
+    wer_line = report[13]
+    (wer,) = re.findall(r"<td>WER</td><td>(\d+(?:\.\d+)?)</td>", wer_line)
+
+    return {"cer": float(cer) / 100, "wer": float(wer) / 100}
+
+
+ocrevalUAtion_path = pathlib.Path("ocrevalUAtion/ocrevalUAtion_results")
+results["ocrevalUAtion"] = {
+    page: parse_ocrevalUAtion_report(ocrevalUAtion_path / f"{page}.report.html") for page in pages
+}
+
+# Load stringalign
+with open("stringalign/stringalign_results/result.json", "r") as f:
+    results["Stringalign"] = json.load(f)
+# Load stringalign
+with open("stringalign/stringalign_results/result_dinglehopper_processing.json", "r") as f:
+    results["Stringalign (Dinglehopper)"] = json.load(f)
+
+# Convert results to records
+result_records = []
+for method, single_method_results in results.items():
+    for sample, sample_result in single_method_results.items():
+        result_records.append({"method": method, "sample": sample, **sample_result})
+
+# Assemble in DataFrame
+df = pd.DataFrame(result_records).sort_values(["sample", "method"]).set_index(["sample", "method"])
+df_no_overall = df.drop("overall", level="sample", errors="ignore")
+df.loc[("overall", "ocrevalUAtion"),] = df.loc[(slice(None), "ocrevalUAtion"),].mean()
+
+dinglehopper_diff_df = df.loc[(slice(None), "Dinglehopper"),].reset_index(level="method", drop=True) - df.loc[
+    (slice(None), "Stringalign (Dinglehopper)"),
+].reset_index(level="method", drop=True)
+df = df.drop("Stringalign (Dinglehopper)", level="method", errors="ignore")
+
+# Get dispersion measures
+mean_absolute_deviation_from_mean = (
+    df.drop("overall", level="sample", errors="ignore")  # This time without the Dinglehopper data as well
+    .groupby("sample")  # Get dataframes for each sample with all methods
+    .transform(
+        lambda s: abs(s - s.median())
+    )  # For each method, compute its absolute deviation from the mean in that sample
+    .groupby("method")  # Get dataframes for each tools with all samples
+    .mean()
+)
+
+overall_res = df.loc["overall"].copy()
+overall_res.columns = pd.MultiIndex.from_tuples([("Overall measure", metric.upper()) for metric in df.columns])
+mean_absolute_deviation_from_mean.columns = pd.MultiIndex.from_tuples(
+    [("MADM", metric.upper()) for metric in df.columns]
+)
+
+out_df = pd.merge(overall_res, mean_absolute_deviation_from_mean, how="outer", on="method")
+with open("hip21_table.tex", "w", encoding="utf-8") as f:
+    f.write(out_df.to_latex(na_rep="--", float_format="%.4f"))
+
+print(out_df)
+print(
+    "Diff between Stringalign and Dinglehopper when confusables are resolved equally\n",
+    dinglehopper_diff_df.drop("overall", errors="ignore").max(),
+)
diff --git a/examples/compare_transcription_eval_tools/hip21_run_analysis.sh b/examples/compare_transcription_eval_tools/hip21_run_analysis.sh
new file mode 100644
index 0000000..4d75dd2
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/hip21_run_analysis.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+# shellcheck disable=all
+run_analysis() {
+
+
+    cd isri
+    rm -rf isri_results
+    time bash run_isri.sh
+    cd ..
+
+    cd jiwer
+    rm -rf jiwer_results
+    time bash run_jiwer.sh
+    cd ..
+
+    cd meeteval
+    rm -rf meeteval_results
+    time bash run_meeteval.sh
+    cd ..
+
+    cd ocrevalUAtion
+    rm -rf ocrevalUAtion_results
+    time bash run_ocrevalUAtion.sh
+    cd ..
+
+    cd stringalign
+    rm -rf stringalign_results
+    time bash run_stringalign.sh
+    cd ..
+
+    cd dinglehopper
+    rm -rf dinglehopper_results
+    time bash run_dinglehopper.sh
+    cd ..
+
+    cd calamari
+    rm -rf calamari_results
+    time bash run_calamari.sh
+    cd ..
+}
+
+rm -rf ocr_results
+
+git clone https://github.com/cneud/hip21_ocrevaluation.git
+cd hip21_ocrevaluation
+git checkout 9979dacfeebef65b419a44ea3f12a0bcba153c6f
+cd ..
+
+pipx run hip21_get_data.py
+run_analysis
+pipx run hip21_get_results.py
diff --git a/examples/compare_transcription_eval_tools/isri/run_isri.Dockerfile b/examples/compare_transcription_eval_tools/isri/run_isri.Dockerfile
new file mode 100644
index 0000000..a6733ce
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/isri/run_isri.Dockerfile
@@ -0,0 +1,14 @@
+FROM python:3.14-trixie
+
+RUN apt-get update && \
+    apt-get install -y build-essential libutf8proc-dev git && \
+    git clone https://github.com/eddieantonio/ocreval && \
+    cd ocreval && \
+    git checkout 873a0de5796c0b9ccf07a549afdd30159a9e0b3e && \
+    make && make install
+
+WORKDIR /analysis
+COPY ocr_results/ ./ocr_results/
+COPY run_isri_analysis.py .
+
+CMD ["python3", "run_isri_analysis.py"]
diff --git a/examples/compare_transcription_eval_tools/isri/run_isri.sh b/examples/compare_transcription_eval_tools/isri/run_isri.sh
new file mode 100644
index 0000000..7b603ed
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/isri/run_isri.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+cp -r ../ocr_results ./ocr_results/
+docker build -f run_isri.Dockerfile -t stringalign_run_isri .
+rm -rf ./ocr_results/
+mkdir -p isri_results
+
+
+docker run --rm  --volume $(pwd)/isri_results:/analysis/output stringalign_run_isri
+docker image rm stringalign_run_isri
diff --git a/examples/compare_transcription_eval_tools/isri/run_isri_analysis.py b/examples/compare_transcription_eval_tools/isri/run_isri_analysis.py
new file mode 100644
index 0000000..ca658d2
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/isri/run_isri_analysis.py
@@ -0,0 +1,30 @@
+import pathlib
+import subprocess
+
+input_dir = pathlib.Path("ocr_results")
+out_dir = pathlib.Path("output")
+
+char_report_files = []
+word_report_files = []
+
+for ref_file in input_dir.glob("*.ref.txt"):
+    name = ref_file.name.partition(".")[0]
+    ref = str(input_dir / f"{name}.ref.txt")
+    pred = str(input_dir / f"{name}.pred.txt")
+
+    char_report_file = str(out_dir / f"{name}.char_report.txt")
+    word_report_file = str(out_dir / f"{name}.word_report.txt")
+
+    subprocess.run(["accuracy", f"ocr_results/{name}.ref.txt", f"ocr_results/{name}.pred.txt", char_report_file])
+
+    subprocess.run(["wordacc", f"ocr_results/{name}.ref.txt", f"ocr_results/{name}.pred.txt", word_report_file])
+
+    char_report_files.append(char_report_file)
+    word_report_files.append(word_report_file)
+
+
+with open(out_dir / "character_report.txt", "w") as f:
+    subprocess.run(["accsum"] + char_report_files, stdout=f)
+
+with open(out_dir / "word_report.txt", "w") as f:
+    subprocess.run(["wordaccsum"] + word_report_files, stdout=f)
diff --git a/examples/compare_transcription_eval_tools/jiwer/run_jiwer.Dockerfile b/examples/compare_transcription_eval_tools/jiwer/run_jiwer.Dockerfile
new file mode 100644
index 0000000..2b6bb55
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/jiwer/run_jiwer.Dockerfile
@@ -0,0 +1,9 @@
+FROM python:3.14-trixie
+
+RUN pip install jiwer==4.0.0
+
+WORKDIR /analysis
+COPY ocr_results/ ./ocr_results/
+COPY run_jiwer.py .
+
+CMD ["python", "run_jiwer.py"]
diff --git a/examples/compare_transcription_eval_tools/jiwer/run_jiwer.py b/examples/compare_transcription_eval_tools/jiwer/run_jiwer.py
new file mode 100644
index 0000000..a5f4c5f
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/jiwer/run_jiwer.py
@@ -0,0 +1,37 @@
+# /// script
+# requires-python = "==3.14.*"
+# dependencies = ["jiwer==4.0.0"]
+# ///
+
+import json
+import pathlib
+
+import jiwer
+
+input_dir = pathlib.Path("ocr_results")
+out_dir = pathlib.Path("output")
+
+char_report_files = []
+word_report_files = []
+
+all_ref = []
+all_pred = []
+
+result = {}
+for ref_file in input_dir.glob("*.ref.txt"):
+    name = ref_file.name.partition(".")[0]
+    ref = (input_dir / f"{name}.ref.txt").read_text(encoding="utf-8")
+    pred = (input_dir / f"{name}.pred.txt").read_text(encoding="utf-8")
+
+    all_ref.append(ref)
+    all_pred.append(pred)
+
+    result[name] = {"cer": jiwer.cer(reference=ref, hypothesis=pred), "wer": jiwer.wer(reference=ref, hypothesis=pred)}
+
+result["overall"] = {
+    "cer": jiwer.cer(reference=all_ref, hypothesis=all_pred),
+    "wer": jiwer.wer(reference=all_ref, hypothesis=all_pred),
+}
+
+with open(out_dir / "result.json", "w") as f:
+    json.dump(result, f)
diff --git a/examples/compare_transcription_eval_tools/jiwer/run_jiwer.sh b/examples/compare_transcription_eval_tools/jiwer/run_jiwer.sh
new file mode 100644
index 0000000..db3607e
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/jiwer/run_jiwer.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+cp -r ../ocr_results ./ocr_results/
+docker build -f run_jiwer.Dockerfile -t stringalign_run_jiwer .
+rm -rf ./ocr_results/
+mkdir -p jiwer_results
+
+
+docker run --rm --volume $(pwd)/jiwer_results:/analysis/output stringalign_run_jiwer
+docker image rm stringalign_run_jiwer
diff --git a/examples/compare_transcription_eval_tools/meeteval/run_meeteval.Dockerfile b/examples/compare_transcription_eval_tools/meeteval/run_meeteval.Dockerfile
new file mode 100644
index 0000000..154cb02
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/meeteval/run_meeteval.Dockerfile
@@ -0,0 +1,9 @@
+FROM python:3.14-trixie
+
+RUN pip install meeteval==0.4.3
+
+WORKDIR /analysis
+COPY ocr_results/ ./ocr_results/
+COPY run_meeteval.py .
+
+CMD ["python", "run_meeteval.py"]
diff --git a/examples/compare_transcription_eval_tools/meeteval/run_meeteval.py b/examples/compare_transcription_eval_tools/meeteval/run_meeteval.py
new file mode 100644
index 0000000..be2a4fd
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/meeteval/run_meeteval.py
@@ -0,0 +1,30 @@
+import json
+import pathlib
+
+import meeteval
+
+input_dir = pathlib.Path("ocr_results")
+out_dir = pathlib.Path("output")
+
+char_report_files = []
+word_report_files = []
+
+result = {}
+error_rates = []
+for ref_file in input_dir.glob("*.ref.txt"):
+    name = ref_file.name.partition(".")[0]
+    ref = (input_dir / f"{name}.ref.txt").read_text(encoding="utf-8")
+    pred = (input_dir / f"{name}.pred.txt").read_text(encoding="utf-8")
+
+    wer = meeteval.wer.wer.siso.siso_word_error_rate(
+        reference=ref,
+        hypothesis=pred,
+    )
+    result[name] = {"wer": wer.error_rate}
+    error_rates.append(wer)
+
+avg = meeteval.wer.combine_error_rates(error_rates)
+result["overall"] = {"wer": avg.error_rate}
+
+with open(out_dir / "result.json", "w") as f:
+    json.dump(result, f, indent=2)
diff --git a/examples/compare_transcription_eval_tools/meeteval/run_meeteval.sh b/examples/compare_transcription_eval_tools/meeteval/run_meeteval.sh
new file mode 100644
index 0000000..53f27a0
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/meeteval/run_meeteval.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+cp -r ../ocr_results ./ocr_results/
+docker build -f run_meeteval.Dockerfile -t stringalign_run_meeteval .
+rm -rf ./ocr_results/
+mkdir -p meeteval_results
+
+
+docker run --rm --volume $(pwd)/meeteval_results:/analysis/output stringalign_run_meeteval
+docker image rm stringalign_run_meeteval
diff --git a/examples/compare_transcription_eval_tools/ocrevalUAtion/container_script.sh b/examples/compare_transcription_eval_tools/ocrevalUAtion/container_script.sh
new file mode 100644
index 0000000..af24079
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/ocrevalUAtion/container_script.sh
@@ -0,0 +1,13 @@
+for reference_file in ocr_results/*.ref.txt
+do
+    name=$(basename "$reference_file" .ref.txt)
+    predicted_file="ocr_results/$name.pred.txt"
+    output_file="output/$name.report.html"
+
+    java -cp ocrevalUAtion-1.3.4-jar-with-dependencies.jar \
+        eu.digitisation.Main \
+        -gt "$reference_file" \
+        -ocr "$predicted_file" \
+        -e utf-8 \
+        -o "$output_file"
+done
diff --git a/examples/compare_transcription_eval_tools/ocrevalUAtion/run_ocrevalUAtion.Dockerfile b/examples/compare_transcription_eval_tools/ocrevalUAtion/run_ocrevalUAtion.Dockerfile
new file mode 100644
index 0000000..28e28c8
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/ocrevalUAtion/run_ocrevalUAtion.Dockerfile
@@ -0,0 +1,13 @@
+FROM python:3.14-trixie
+
+WORKDIR /analysis
+RUN apt-get update && \
+    apt-get install -y openjdk-25-jre-headless wget && \
+    wget https://github.com/impactcentre/ocrevalUAtion/releases/download/v1.3.4/ocrevalUAtion-1.3.4-jar-with-dependencies.jar
+
+
+WORKDIR /analysis
+COPY ocr_results/ ./ocr_results/
+COPY container_script.sh run_eval.sh
+
+CMD ["bash", "run_eval.sh"]
diff --git a/examples/compare_transcription_eval_tools/ocrevalUAtion/run_ocrevalUAtion.sh b/examples/compare_transcription_eval_tools/ocrevalUAtion/run_ocrevalUAtion.sh
new file mode 100644
index 0000000..cec7a49
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/ocrevalUAtion/run_ocrevalUAtion.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+
+cp -r ../ocr_results ./ocr_results
+docker build -f run_ocrevalUAtion.Dockerfile -t stringalign_run_ocrevaluation .
+rm -rf ocr_results
+mkdir -p ocrevalUAtion_results
+
+docker run --rm --volume $(pwd)/../ocr_results:/analysis/ocr_results --volume $(pwd)/ocrevalUAtion_results:/analysis/output stringalign_run_ocrevaluation
+docker image rm stringalign_run_ocrevaluation
diff --git a/examples/compare_transcription_eval_tools/stringalign/run_stringalign.Dockerfile b/examples/compare_transcription_eval_tools/stringalign/run_stringalign.Dockerfile
new file mode 100644
index 0000000..4d04ec4
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/stringalign/run_stringalign.Dockerfile
@@ -0,0 +1,9 @@
+FROM python:3.13-trixie
+
+RUN pip install stringalign==0.1.4
+
+WORKDIR /analysis
+COPY ocr_results/ ./ocr_results/
+COPY run_stringalign.py .
+
+CMD ["python", "run_stringalign.py"]
diff --git a/examples/compare_transcription_eval_tools/stringalign/run_stringalign.py b/examples/compare_transcription_eval_tools/stringalign/run_stringalign.py
new file mode 100644
index 0000000..7be68f2
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/stringalign/run_stringalign.py
@@ -0,0 +1,136 @@
+# /// script
+# requires-python = "==3.14.*"
+# dependencies = ["stringalign"]
+# ///
+
+import json
+import pathlib
+
+import stringalign
+
+input_dir = pathlib.Path("ocr_results")
+out_dir = pathlib.Path("output")
+char_tokenizer = stringalign.tokenize.GraphemeClusterTokenizer()
+word_tokenizer = stringalign.tokenize.SplitAtWhitespaceTokenizer()
+
+dinglehopper_normalizer = stringalign.normalize.StringNormalizer(
+    # These confusable mappings are copied from Dinglehopper's source code.
+    # Dinglehopper is Apache 2.0 licensed, which is compatible with the MIT license.
+    # See the Dinglehopper source code for more information
+    # https://github.com/qurator-spk/dinglehopper/blob/master/src/dinglehopper/extracted_text.py
+    resolve_confusables={
+        "": "ſſ",
+        "\ueba7": "ſſi",  # MUFI: LATIN SMALL LIGATURE LONG S LONG S I
+        "": "ch",
+        "": "ck",
+        "": "ll",
+        "": "ſi",
+        "": "ſt",
+        "fi": "fi",
+        "ff": "ff",
+        "fl": "fl",
+        "ffi": "ffi",
+        "": "ct",
+        "": "tz",  # MUFI: LATIN SMALL LIGATURE TZ
+        "\uf532": "as",  # eMOP: Latin small ligature as
+        "\uf533": "is",  # eMOP: Latin small ligature is
+        "\uf534": "us",  # eMOP: Latin small ligature us
+        "\uf535": "Qu",  # eMOP: Latin ligature capital Q small u
+        "ij": "ij",  # U+0133 LATIN SMALL LIGATURE IJ
+        "\ue8bf": "q&",
+        # MUFI: LATIN SMALL LETTER Q LIGATED WITH FINAL ET
+        # XXX How to replace this correctly?
+        "\ueba5": "ſp",  # MUFI: LATIN SMALL LIGATURE LONG S P
+        "st": "st",  # U+FB06 LATIN SMALL LIGATURE ST
+    }
+    | {
+        "": "ü",
+        "": "ä",
+        "==": "–",  # → en-dash
+        "—": "–",  # em-dash → en-dash
+        "": "ö",
+        "’": "'",
+        "⸗": "-",
+        "aͤ": "ä",  # LATIN SMALL LETTER A, COMBINING LATIN SMALL LETTER E
+        "oͤ": "ö",  # LATIN SMALL LETTER O, COMBINING LATIN SMALL LETTER E
+        "uͤ": "ü",  # LATIN SMALL LETTER U, COMBINING LATIN SMALL LETTER E
+        "\uf50e": "q́",  # U+F50E LATIN SMALL LETTER Q WITH ACUTE ACCENT
+    }
+)
+dinglehopper_char_tokenizer = stringalign.tokenize.GraphemeClusterTokenizer(
+    pre_tokenization_normalizer=dinglehopper_normalizer
+)
+dinglehopper_word_tokenizer = stringalign.tokenize.UnicodeWordTokenizer(
+    pre_tokenization_normalizer=dinglehopper_normalizer
+)
+
+char_report_files = []
+word_report_files = []
+
+all_ref = []
+all_pred = []
+
+result = {}
+result_dinglehopper_confusable_handling = {}
+for ref_file in input_dir.glob("*.ref.txt"):
+    name = ref_file.name.partition(".")[0]
+    ref = "\n".join((input_dir / f"{name}.ref.txt").read_text(encoding="utf-8").splitlines())
+    pred = "\n".join((input_dir / f"{name}.pred.txt").read_text(encoding="utf-8").splitlines())
+
+    all_ref.append(ref)
+    all_pred.append(pred)
+
+    result[name] = {
+        "cer": (
+            stringalign.statistics.StringConfusionMatrix.from_strings(
+                reference=ref, predicted=pred, tokenizer=char_tokenizer
+            ).compute_token_error_rate()
+        ),
+        "wer": (
+            stringalign.statistics.StringConfusionMatrix.from_strings(
+                reference=ref, predicted=pred, tokenizer=word_tokenizer
+            ).compute_token_error_rate()
+        ),
+    }
+    result_dinglehopper_confusable_handling[name] = {
+        "cer": (
+            stringalign.statistics.StringConfusionMatrix.from_strings(
+                reference=ref, predicted=pred, tokenizer=dinglehopper_char_tokenizer
+            ).compute_token_error_rate()
+        ),
+        "wer": (
+            stringalign.statistics.StringConfusionMatrix.from_strings(
+                reference=ref, predicted=pred, tokenizer=dinglehopper_word_tokenizer
+            ).compute_token_error_rate()
+        ),
+    }
+
+result["overall"] = {
+    "cer": (
+        stringalign.statistics.StringConfusionMatrix.from_string_collections(
+            references=all_ref, predictions=all_pred, tokenizer=char_tokenizer
+        ).compute_token_error_rate()
+    ),
+    "wer": (
+        stringalign.statistics.StringConfusionMatrix.from_string_collections(
+            references=all_ref, predictions=all_pred, tokenizer=word_tokenizer
+        ).compute_token_error_rate()
+    ),
+}
+result_dinglehopper_confusable_handling["overall"] = {
+    "cer": (
+        stringalign.statistics.StringConfusionMatrix.from_string_collections(
+            references=all_ref, predictions=all_pred, tokenizer=dinglehopper_char_tokenizer
+        ).compute_token_error_rate()
+    ),
+    "wer": (
+        stringalign.statistics.StringConfusionMatrix.from_string_collections(
+            references=all_ref, predictions=all_pred, tokenizer=dinglehopper_word_tokenizer
+        ).compute_token_error_rate()
+    ),
+}
+
+with open(out_dir / "result.json", "w") as f:
+    json.dump(result, f)
+with open(out_dir / "result_dinglehopper_processing.json", "w") as f:
+    json.dump(result_dinglehopper_confusable_handling, f)
diff --git a/examples/compare_transcription_eval_tools/stringalign/run_stringalign.sh b/examples/compare_transcription_eval_tools/stringalign/run_stringalign.sh
new file mode 100644
index 0000000..0505ee7
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/stringalign/run_stringalign.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+cp -r ../ocr_results ./ocr_results/
+docker build -f run_stringalign.Dockerfile -t stringalign_run_stringalign .
+rm -rf ./ocr_results/
+mkdir -p stringalign_results
+
+
+docker run --rm --volume $(pwd)/stringalign_results:/analysis/output stringalign_run_stringalign
+docker image rm stringalign_run_stringalign
diff --git a/examples/compare_transcription_eval_tools/synthetic_emoji_get_results.py b/examples/compare_transcription_eval_tools/synthetic_emoji_get_results.py
new file mode 100644
index 0000000..c7b9f43
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/synthetic_emoji_get_results.py
@@ -0,0 +1,98 @@
+# /// script
+# requires-python = ">=3.13"
+# dependencies = ["pandas", "jinja2"]
+# ///
+
+"""
+This script gets the results from all the string comparison tools and creates a single DataFrame to display them
+"""
+
+import json
+import pathlib
+import re
+
+import pandas as pd
+
+pages = [
+    "page00000",
+    "page00001",
+    "page00002",
+    "page00003",
+    "page00004",
+    "page00005",
+]
+
+results = {}
+
+
+# Load Calamari results
+with open("calamari/calamari_results/result.json", "r") as f:
+    results["calamari"] = {"cer": json.load(f)["overall"]["avg_ler"]}
+
+
+# Load Dinglehopper results
+with open("dinglehopper/dinglehopper_results/page00000.txt-report.json", "r") as f:
+    summary_data = json.load(f)
+results["dinglehopper"] = {"cer": summary_data["cer"], "wer": summary_data["wer"]}
+
+
+# Load ISRI results
+def get_isri_error_rate(file: pathlib.Path) -> float:
+    with open(file, "r") as f:
+        report = f.readlines()
+
+    accuracy_line = report[4]
+    (accuracy_match,) = re.findall(r"(\d+(?:\.\d+)?)%\s+Accuracy", accuracy_line)
+    return 1 - float(accuracy_match) / 100
+
+
+isri_path = pathlib.Path("isri/isri_results")
+results["isri"] = {
+    "cer": get_isri_error_rate(isri_path / "page00000.char_report.txt"),
+    "wer": get_isri_error_rate(isri_path / "page00000.word_report.txt"),
+}
+
+# Load jiwer
+jiwer_path = pathlib.Path("jiwer/jiwer_results")
+with open("jiwer/jiwer_results/result.json", "r") as f:
+    results["jiwer"] = json.load(f)["overall"]
+
+# Load meeteval
+jiwer_path = pathlib.Path("meeteval/meeteval_results")
+with open("meeteval/meeteval_results/result.json", "r") as f:
+    results["meeteval"] = json.load(f)["overall"]
+
+
+# Load ocrevalUAtion
+def parse_ocrevalUAtion_report(file: pathlib.Path) -> dict[str, float]:
+    with open(file, "r") as f:
+        report = f.readlines()
+    cer_line = report[10]
+    (cer,) = re.findall(r"<td>CER</td><td>(\d+(?:\.\d+)?)</td>", cer_line)
+
+    wer_line = report[13]
+    (wer,) = re.findall(r"<td>WER</td><td>(\d+(?:\.\d+)?)</td>", wer_line)
+
+    return {"cer": float(cer) / 100, "wer": float(wer) / 100}
+
+
+try:
+    ocrevalUAtion_path = pathlib.Path("ocrevalUAtion/ocrevalUAtion_results")
+    results["ocrevalUAtion"] = parse_ocrevalUAtion_report(ocrevalUAtion_path / "page00000.report.html")
+except Exception:
+    print("ocrevalUAtion failed")
+
+# Load stringalign
+with open("stringalign/stringalign_results/result.json", "r") as f:
+    results["stringalign"] = json.load(f)["overall"]
+
+# Convert results to records
+result_records = []
+for method, single_method_results in results.items():
+    result_records.append({"method": method, **single_method_results})
+
+df = pd.DataFrame(result_records).sort_values(["method"]).set_index(["method"])
+
+print(df)
+with open("synthetic_emoji_table.tex", "w", encoding="utf-8") as f:
+    f.write(df.to_latex(na_rep="--", float_format="%.4f"))
diff --git a/examples/compare_transcription_eval_tools/synthetic_emoji_make_data.sh b/examples/compare_transcription_eval_tools/synthetic_emoji_make_data.sh
new file mode 100644
index 0000000..2403dd5
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/synthetic_emoji_make_data.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+rm -r ocr_results
+mkdir ocr_results
+printf "That was\nclose! 😮‍💨" > ocr_results/page00000.ref.txt
+printf "That was\nclose!" > ocr_results/page00000.pred.txt
diff --git a/examples/compare_transcription_eval_tools/synthetic_emoji_run_analysis.sh b/examples/compare_transcription_eval_tools/synthetic_emoji_run_analysis.sh
new file mode 100644
index 0000000..d41715c
--- /dev/null
+++ b/examples/compare_transcription_eval_tools/synthetic_emoji_run_analysis.sh
@@ -0,0 +1,44 @@
+#!/bin/bash
+
+run_analysis() {
+    cd calamari
+    rm -rf calamari_results
+    bash run_calamari.sh
+    cd ..
+
+
+    cd dinglehopper
+    rm -rf dinglehopper_results
+    bash run_dinglehopper.sh
+    cd ..
+
+
+    cd isri
+    rm -rf isri_results
+    bash run_isri.sh
+    cd ..
+
+    cd jiwer
+    rm -rf jiwer_results
+    bash run_jiwer.sh
+    cd ..
+
+    cd meeteval
+    rm -rf meeteval_results
+    bash run_meeteval.sh
+    cd ..
+
+    cd ocrevalUAtion
+    rm -rf ocrevalUAtion_results
+    bash run_ocrevalUAtion.sh
+    cd ..
+
+    cd stringalign
+    rm -rf stringalign_results
+    bash run_stringalign.sh
+    cd ..
+}
+
+bash synthetic_emoji_make_data.sh
+run_analysis
+pipx synthetic_emoji_get_results.py