Gains:
- Ability to set up the HTR and transliteration workflow and explain why the raw draft requires expert checking line by line
- Ability to protect unreadable areas by asking for alternatives instead of imposing a precise reading in case of vowel/letter ambiguity in Ottoman Turkish
- Ability to separate the original transliteration from a separate layer of simplification, keeping final scientific responsibility with the paleographer
Perhaps the most demanding part of historical research is transcribing manuscripts and old written documents into readable text. A letter, a notebook, a court record or an Ottoman document becomes searchable only after it has been transcribed. Transcription is the act of transferring the contents of a document (most often handwritten or ancient script) into a written, readable text. In the case of Ottoman, this is often accompanied by transliteration: transferring the text in Arabic letters into the Latin alphabet with its letter-by-letter equivalents.
We used OCR for printed texts; Handwriting requires a different technology: HTR (Handwritten Text Recognition). HTR is a technology similar to OCR but much more challenging that converts handwritten documents into machine text. In this unit we will see how to use HTR and AI in Ottoman and manuscript transcription, the margins of error and why verification is even more critical here.
Why is handwriting so difficult?
Handwriting is much more variable than printed text: each writer has a unique hand, letters are linked together, abbreviations and special signs are used, ink bleeds, paper wears out. In Ottoman Turkish the difficulty is multiplied:
- Contextual letter forms: The same letter is written differently at the beginning, middle and end of the word.
- Not writing vowels: Short vowels are often not written; the reader completes from the context. This creates ambiguities such as "acceptance or rejection?"
- Different writing styles: There are different handwriting styles such as rik'a, divani, ta'lik; Each requires a different reading expertise.
- Ottoman vocabulary: Words from Arabic and Persian that do not exist in today's Turkish.
Therefore, HTR and AI work with a much higher margin of error in Ottoman Turkish than print OCR. The eye of an expert paleographer (paleography - the science of reading ancient writings) is not a tool here, but a necessity.
Workflow: step by step
1. Prepare document quality. As with OCR, high resolution and good contrast are essential. This is even more critical in handwriting.
2. Select HTR model. Ottoman, Arabic handwriting, or HTR models specifically trained for a specific period are much more successful than general models. Test which model works well for a period and writing style.
3. Generate raw transcription. The HTR model produces an outline. In Ottoman Turkish, this draft is a beginning full of errors that an experienced eye must check thoroughly.
4. Contextual improvement with AI. You can have the AI flag inconsistencies, possible reading alternatives, and suspicious locations in the raw output. But the AI “correction” is particularly risky here: it could suggest a contrived reading.
5. Transliteration and simplification (layered). The transliteration of the text in Arabic letters into Latin letters, followed by its simplified reading into today's Turkish, is produced as separate layers. The original never breaks.
6. Expert verification. The final transcription is approved by an expert with paleography and period knowledge by comparing it with the document.
Attention: In Ottoman Turkish, when "guessing" a word with an unwritten vowel from the context, the AI can produce a completely wrong reading and present it in a confident language. A difference in letters, such as "kabul" instead of "kabil" or "alem" instead of "alim", completely changes the meaning. Every uncertain reading should be presented with alternatives, and no single definitive reading should be imposed.
Layers and responsibility with a table
layer
Content
Role of AI
Role of the expert
Image
original document
—
Source
HTR draft
Raw machine reading
produces
Control from start to finish
transliteration
Latin letter transposition
draft suggests
Corrects, corrects
Notes of uncertainty
[?] and alternatives
signs
decides
Simplification
Today's Turkish
draft suggests
Approvals
scientific publication
Final text + notes
—
Expert only
three mini cases
Case 1 — HTR speeded up the first draft by 5x. A doctoral student would transcribe a 200-page Ottoman notebook. Full manual transcription was estimated at 60 business days. With an HTR model trained for the period, the raw draft came out in a few hours; The student corrected this draft and reduced the work to 12 working days. But he had to compare each page to the document, line by line; About 30% of the draft was incorrect.
Case 2 — One letter, one meaning. One researcher relied on a word the AI read as "governor." Under expert control, it was understood that the word was actually "guardian" (responsible for a child/person), and since the vowel was not marked, the AI guessed it wrong. The legal meaning of the document was changing completely. Lesson: In Ottoman Turkish, a single letter/vowel difference causes the document to be interpreted from the beginning; expert is required.
Case 3 — Contrived completion. In a line whose ink had run out and one word of which was unreadable, the AI filled the gap with a “logical” word. The expert realized that that gap was actually a place name and that the AI had made it up. Space left as [illegible]. Lesson: unreadable space is not filled, it is marked.
Four copyable templates
1) Transliteration that preserves ambiguity:
Below is the HTR raw output/transliteration of an Ottoman document. Your task is to review the text and flag possible reading errors. Rules: (1) Offer 2-3 possible reading alternatives for each word you are not sure about, do not impose one. (2) Leave [illegible] in the illegible areas, do not fill them. (3) ADDING words that do not exist in the text. (4) Show alternatives in words with ambiguous vowels. Text: [here]
2) Layered reading (original + simplification):
Generate TWO columns for the following verified Ottoman transliteration: (1) Original transliteration (touch), (2) Simplified reading into today's Turkish. ADDING meaning, adding interpretation in simplification; just update the language. Mark places with ambiguous meaning[indistinct]. Transliteration: [here]
3) Term and person extraction:
The personal names, place names, titles and periodic terms mentioned in the transcription below appear in separate lists. Take only those CLEARLY mentioned in the text; Do not add by guessing. Mark with [?] if you are not sure of the pronunciation. Text: [here]
4) Suspicious read control:
An experienced paleography controller in the role. In the transcription below, mark the words that are inconsistent in meaning, do not fit the period or do not fit into the context, and write why they are suspicious. Imposing definitive correction; Generate a list of suspicions that the human expert will compare with the document. Text: [here]
Weak prompt / Strong prompt
Weak:
Read this Ottoman text and give me its translation.
This pushes the AI to produce a single, definitive reading, hiding ambiguities and fitting in gaps. In Ottoman Turkish, this is almost a guaranteed mistake.
Strong:
Check out the Ottoman transliteration below. A definitive reading: IMPOSITION. Provide possible alternatives for each ambiguous word, leave out [illegible] parts, do not add anything that is not in the text. Give the simplified reading to today's Turkish in a separate column, but keep the original. Mark anything you are not sure about with [?] so that the expert can compare it with the document. Text: [here]
The difference: strict read prohibition, requesting alternatives, preserving whitespace, and layered output enable expert verification.
Common mistakes
- Mistaking the HTR draft as correct. In Ottoman Turkish, much of the raw draft may be inaccurate; Line by line control is a must.
- The only definitive reading is to impose. If alternatives are hidden in words whose vowels are not written, the wrong meaning will be recorded.
- Filling in the unreadable area. It should be protected by whitespace [illegible], not made up.
- Mixing the original with simplification. Simplification should be a separate layer; The original transliteration should not be distorted.
- Disabling the expert. Without knowledge of paleography and period, AI's reading is a guess with no scientific value.
In summary
Ottoman and manuscript transcription can be greatly accelerated at the raw draft stage with HTR and AI; You get a first output that reduces days of work to hours. But it is precisely in this area that a single letter, a single vowel difference changes the meaning; AI tends to hide uncertainty and fill in the gaps. The correct method is layered: raw outline, alternative notes of ambiguity, a separate layer of simplification and, above all, line-by-line verification of the document by an expert familiar with paleography. AI speeds up initial reading; Scientific responsibility belongs to the expert.
Application task
Obtain a transliteration of an Ottoman or manuscript document (your own production or a published copy). Ask the AI for an alternative reading with the “ambiguity-preserving transliteration” template. Then generate the simplification layer separately with "layered reading". Compare each vaguely marked word with an expert source or dictionary; Note how many errors the AI would produce if it imposed a single reading.
checklist
- [ ] I used an HTR/model appropriate to the period and writing style.
- [ ] I asked the AI for alternatives to the exact reading.
- [ ] I protected the unreadable parts with [unreadable].
- [ ] I kept the original transliteration separate from the simplification.
- [ ] I had the final text verified by an expert/documentary who knows paleography.