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Start with a representative sample of your own documents. Identify the scripts and writing direction, test more than one workflow where possible, and retain the page image beside the recognized text. That approach is more reliable than choosing a tool from a headline accuracy number.
What Hán-Nôm collections actually contain
Hán-Nôm is not a single uniform OCR problem. The National Library of Vietnam and the Vietnamese Nôm Preservation Foundation describe holdings that include woodblock-printed books, handwritten material, contemporary typesetting, and pages that combine Hán, Nôm, and occasional quốc ngữ transliteration. Subjects range from classics, history, philosophy, literature, law, medicine, and religion to government and other practical writing. Their project overview says that much Vietnamese literature, philosophy, history, law, medicine, religion, and government policy from the 10th through the 20th centuries was written in chữ Nôm.
The same overview reports more than 4,000 texts in the National Library’s special collection. That is a figure for that described collection, not a census of every surviving Hán-Nôm document. Its variety explains why a tool trained on one literary edition may behave very differently on another archive.
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Hán, Nôm, mixed pages, and transliteration
Before selecting software, classify the pages. Hán characters may follow conventions associated with Literary Chinese, while Nôm uses a large and complex inventory of characters, including forms created or adapted to represent Vietnamese. A mixed page can switch scripts within a line, add a modern Vietnamese gloss, or place annotations around the main text. Ask whether a system represents variant forms explicitly, maps them to a preferred Unicode character, or silently substitutes a visually similar character.
Printed, handwritten, and typeset sources
Woodblock print often gives a recognisable baseline for OCR, but ink spread, broken strokes, uneven impressions, page bleed-through, and wormholes still create errors. Handwriting introduces individual ductus, abbreviations, ligatures, and inconsistent spacing. Modern typesetting may have cleaner characters but unusual fonts or editorial conventions. A result reported on a printed literary work therefore does not establish performance on handwriting or on a different collection.
Define the output before you compare tools
“OCR” can mean several different deliverables. Specify which one your project requires:
- Searchable text: a fast, imperfect layer for finding names, places, or repeated phrases.
- Character recognition: a sequence of Hán or Nôm characters that can be checked against the image.
- Diplomatic transcription: a record that preserves page order, line breaks, variant forms, punctuation, and marginal material.
- Modern Vietnamese rendering: a translation, reading, or quốc ngữ representation. Recognition software alone does not supply a reliable scholarly translation.
A project that only needs full-text discovery can accept more uncertainty than an edition intended for quotation. Keep the recognized character sequence separate from any normalized reading or translation so later editors can see what changed.
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1. Inventory a representative sample
Select pages that include the best and worst conditions in the collection: clear and dark impressions, faded pages, annotations, different volumes, and every script combination you expect. Record whether writing is vertical or horizontal, how many columns appear, where reading begins, and whether notes interrupt the main text. A single pristine page can produce a misleadingly optimistic test.
2. Describe page condition and structure
Note stains, tears, skew, bleed-through, missing corners, compression artifacts, and uneven illumination. Mark pages with multiple columns, interlinear glosses, seals, illustrations, or rotated marginal notes. Layout analysis and image degradation are recurring difficulties in historical-document research; recognition quality depends on isolating the right text regions before characters are decoded.
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3. Check character and variant coverage
Ask how uncommon Nôm characters are encoded and what happens when a character is absent from the model’s inventory. A system may return a substitute, an unknown symbol, or no character at all. Establish a policy for variants before processing thousands of pages: preserve the printed form, map to a canonical Unicode character, or record both.
4. Decide how review will work
Prefer an interface or pipeline that keeps the original page visible beside the output, lets an editor correct one character without retyping a line, and records uncertainty. If a service offers dictionary lookup, manuscript notation, or links between image regions and text, those features can matter more to a scholarly workflow than a marginal difference in an aggregate score.
5. Verify access and data terms
The sources describing the tools do not establish current prices, commercial access conditions, upload-retention periods, privacy guarantees, or service-level commitments. Confirm those terms directly before uploading restricted, unpublished, or culturally sensitive scans. For an offline research environment, verify that the model, dependencies, and character fonts can be installed and preserved with the project.
Named Hán-Nôm OCR resources and what their evidence shows
The following resources are useful starting points, but their evidence is not a common benchmark. They differ in data, metrics, and purpose.
| Resource | What it provides or reports | Best reason to investigate | What the evidence does not prove |
|---|---|---|---|
| Digitizing Việt Nam Hán-Nôm Research Hub | A project page describes a mirror of Columbia University’s Hán-Nôm Sino-Vietnamese Textual Library, automated OCR, searchable dictionary lookup, manuscript notation, and interfaces with MARKUS and IMMARKUS. It describes a dedicated Hán-Nôm OCR tool with variant-aware models for scholarly use. | An integrated environment for moving from page image to lookup, annotation, and textual research. | The project description states scope and intended features; it is not an independently controlled accuracy test across collections. |
| NomNaOCR | Its project README reports 2,953 pages and 38,318 image patches for historical Sino-Nôm detection and recognition. The documented workflow rotates pages to accommodate vertical text and reports sequence accuracy, character accuracy, and character error rate. | A research pipeline whose documented data and evaluation terminology can help you design your own test. | The dataset scale and reported methods do not guarantee performance on another archive, script mixture, or page condition. |
| Published Nôm OCR study, 2021 | A Vietnam Academy of Science and Technology paper describes a fine-tuned Tesseract approach and a CNN-based model. Training used Truyện Kiều (1871); testing used Truyện Kiều (1902) and Truyện Lục Vân Tiên. | A published example of adapting recognition to historical Nôm editions. | Its scores belong to those datasets, models, and metric definitions, not to every Nôm book. |
| PaddleOCRv5 fine-tuning preprint, 2025 | Nguyen and Su report exact accuracy rising from 37.5% to 50.0% after fine-tuning the recognition module in their curated study. | Evidence that domain-specific fine-tuning can improve a general recognition system. | This is a preprint result on one evaluation set, not an independent comparison with the other resources. |
How to run a defensible OCR pilot
- Create archival page images. Use a stable overhead book-scanning setup or another non-destructive capture method appropriate to the binding. Preserve an untouched master image and make separate working copies for cropping, rotation, and contrast adjustments. The archive and research literature establish digitized page images as a foundation of the workflow; no scanner model has been shown here to improve Hán-Nôm recognition by itself.
- Name and organize files. Give each image a persistent identifier that records collection, volume, and page order. Keep foldouts, blank pages, covers, and inserts in sequence even if they contain no OCR text.
- Prepare a small test set. Include several pages from each document type and condition. If vertical writing is present, test the orientation expected by the recognition pipeline and retain the unrotated original.
- Run the same sample through each candidate. Do not change the sample between tools. Record model version, preprocessing, orientation, segmentation choices, and date so another editor can reproduce the trial.
- Align output to image regions. Check that columns, lines, marginal notes, and seals have not been merged. A correctly recognized character in the wrong reading order is still a transcription error.
- Correct against the page. Work line by line, mark uncertain characters, and use a second pass for names, numbers, dates, place names, and technical terms. Preserve the machine output in a separate layer rather than overwriting it.
- Measure on your own ground truth. Have a qualified reader create a checked reference for a modest sample. Compare character errors and sequence errors by page type, not only as one collection-wide average.
- Decide whether the gain justifies scale-up. If a tool saves review time on clean pages but fails on the pages that matter most, use it selectively or invest in adaptation instead of applying it indiscriminately.
Choosing by document scenario
Clean or moderately clean woodblock print
Begin with a tool or model documented on printed historical material. Test representative editions rather than assuming that all woodblock type is equivalent. Separate page segmentation from character recognition: a system can identify characters well while reading columns in the wrong order.
Vertical multi-column pages
Confirm that the workflow handles vertical text and rotated images. NomNaOCR’s documentation describes rotating images to improve text-box detection for vertical pages, illustrating why orientation is a first-class setting rather than a cosmetic adjustment. Check reading order, interlinear notes, and right-to-left column progression manually.
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Handwritten manuscripts
Use printed-text results only as a lower-bound indication of relevance, not as evidence of handwriting accuracy. Build a handwriting-specific sample, expect more manual correction, and determine whether the tool allows region-by-region annotation. If no candidate handles the script reliably, searchable indexing of selected fields may be more practical than attempting a full automatic transcription.
Mixed Hán-Nôm and quốc ngữ
Test script changes within the same line and page. Decide whether the output should preserve each script, normalize variants, or add a separate modern reading. A pipeline that assumes one alphabet may silently replace less common Nôm characters, so inspect substitutions rather than relying on a clean-looking text file.
Damaged, faded, or irregular pages
Do not hide damage with aggressive enhancement. Keep the original and document every crop, rotation, or contrast operation. Process clear regions first, flag unreadable areas, and avoid presenting guesses as transcriptions. Human review is essential when strokes are missing or bleed-through changes the apparent character.
How to interpret reported accuracy numbers
Published figures are useful only with their dataset and metric attached. NomNaOCR describes sequence accuracy, character accuracy, and character error rate; these measure different aspects of a predicted sequence. The 2021 study reports top-1 mAP of 71.80% and top-5 mAP of 80.54% on its Truyện Kiều (1902) test dataset, and top-1 mAP of 72.96% and top-5 mAP of 82.36% on its Truyện Lục Vân Tiên test dataset. Those mAP figures should not be compared directly with character accuracy or exact accuracy from another project.
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The 2025 PaddleOCRv5 preprint reports exact accuracy improving from 37.5% to 50.0% in its own curated evaluation. Exact-match scoring is stringent and can count a sequence as wrong when one character differs, while other metrics may average over individual characters or detected regions. None of these results establishes a universal ranking of the Hán-Nôm Research Hub, NomNaOCR, Tesseract-based work, or PaddleOCRv5 fine-tuning.
Build a local scorecard
For a meaningful decision, score each candidate on the pages you actually need:
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- Character accuracy for clear, isolated lines.
- Sequence or line accuracy for usable search and transcription units.
- Character error rate, with insertions, deletions, and substitutions recorded separately.
- Layout accuracy: column order, line grouping, and treatment of marginalia.
- Coverage of rare and variant characters.
- Editor time per page, including correction and uncertainty marking.
Report results by source type and condition. A single average can conceal the fact that one system is excellent on clean print and unusable on handwriting.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Human verification is part of the method
Historical OCR should be treated as an aid to indexing and transcription, not an authority. Keep the page image and recognized text synchronized so an editor can inspect every disputed reading. Use dictionary lookup or manuscript notation where available, but do not let a dictionary automatically replace a character without recording the original form.
A workable review sequence
- Read the line in the image before looking at the proposed text when confirmation bias is a concern.
- Mark characters that are blurred, damaged, variant, or supplied by context rather than clearly visible.
- Compare repeated words and parallel passages, but verify that the apparent repetition is not an OCR copy error.
- Have a second reader review high-impact passages, names, dates, quotations, and editorial interventions.
- Store the corrected transcription, the machine draft, the image identifier, and the reviewer’s notes as separate records.
This separation makes later model improvement possible and prevents an untraceable correction from being mistaken for what the page actually shows.
Digitization choices that affect recognition
OCR cannot recover information that was never captured. Use lighting that does not create glare, support the binding without flattening it dangerously, and capture enough detail to distinguish fine strokes. Correct page skew and crop consistently, but retain the full page so annotations and physical context remain available. For bound volumes, an overhead scanner can be a task-enabling part of the imaging workflow; the available evidence does not identify a particular model as superior for Hán-Nôm recognition.
Preserve color masters when ink, paper tone, seals, or annotations carry meaning. Generate derivative images for segmentation and recognition, and record which derivative produced each text file. If pages are re-cropped or reprocessed, increment the derivative version rather than replacing the earlier one.
Common failure modes and responses
Characters look plausible but are wrong
Visual similarity and contextual guessing can produce fluent-looking errors. Compare the glyph with the image, inspect rare-character substitutions, and require verification for names and technical vocabulary.
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Text is in the wrong order
Revisit orientation, column detection, and marginal-note rules. Re-running recognition without correcting layout will not fix a sequencing problem.
Output omits unusual characters
Check the model’s character inventory and encoding policy. Record an explicit unknown or unresolved mark during review instead of silently inserting a common substitute.
Scores collapse on a new volume
Look for differences in edition, font, handwriting, damage, or page structure. The documented datasets were curated for particular works; domain adaptation or a narrower indexing goal may be needed.
A cloud workflow is unsuitable for restricted material
Pause uploads until retention, privacy, access, and deletion terms are confirmed. Consider an offline pipeline, a local model, or processing only a permitted derivative.
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Choosing a workflow for your project goal
| Project goal | Recommended emphasis | Acceptable stopping point |
|---|---|---|
| Find references across many volumes | Fast page segmentation, searchable output, and a visible uncertainty flag. | Machine text used as an index, with image confirmation before citation. |
| Prepare a scholarly transcription | Variant handling, image-linked correction, provenance, and second-reader review. | Every published reading checked against the page and documented. |
| Compare editions | Stable page identifiers, consistent column order, and preservation of original forms. | Separate diplomatic layers from normalized readings. |
| Train or adapt a model | Representative ground truth, documented annotations, and evaluation split by document type. | Reported metrics that another researcher can reproduce. |
What a careful choice looks like
Choose the tool that fits your hardest important pages, not the one with the most impressive isolated number. The Hán-Nôm Research Hub is worth examining when integrated lookup, notation, and textual-research connections match your work. NomNaOCR is informative when you need a documented historical Sino-Nôm detection and recognition pipeline, especially for vertical layouts. The 2021 Nôm study and the 2025 PaddleOCRv5 preprint show how specialized training can help, but their reported scores remain tied to their own data and evaluation methods.
For any option, begin with a pilot, preserve the image as the authority, measure on representative pages, and budget for correction. That discipline turns OCR from an opaque transcription substitute into a controlled aid for discovering and studying historical documents.
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