Turn a box of handwritten letters into a family history you can search

A box of letters may contain a family’s travels, ordinary worries and repeated references to people whose names you half remember. Reading one letter is intimate. Finding every mention of the same place across the box is a research project. AI handwriting recognition can help turn that collection into something you can explore without replacing the handwriting itself.

Transkribus provides a browser-based route from a photographed or scanned page to editable text. Its tools support handwriting recognition, corrections, tags and searching across transcribed documents. The workflow below is our proposed use of those documented features; it was not tested on a family’s correspondence for this article.[1]

Start with ten pages, not the whole attic

Choose a small connected group: letters from one person, or pages about one journey. Ten pages is an editorial suggestion, not a limit. Include envelopes and backs where they supply dates or addresses. Before uploading private correspondence to a cloud service, agree with the relevant people what may be processed and keep sensitive material out of the first exercise.

Photograph each page square-on in even light, with the edges and any marginal writing visible. Keep a full-resolution original outside the transcription service. Give the images stable names such as Letter-001-page-01 and record which envelope belongs with them. These simple preparation choices make it easier to trace a later claim back to the object that supports it.

The Transkribus letter demo accepts PNG or JPG images up to 10 MB and offers a free trial. Its account offer describes monthly credits, which should not be mistaken for an identical number of pages under every recognition model. Check the account’s current credit requirement before processing a large collection.[1]

Run a small sample through a handwriting model chosen for the material. In Transkribus, filter public text-recognition models by handwritten versus printed material, language, period and script; inspect the model description and character error rate, then use Quick Text Recognition where available. Select the pages, choose Process with AI, confirm the credit estimate and start recognition. There is no universal model for every hand, so compare two plausible models on the same pages before committing the whole collection.[1][5][6]

Correct the words that carry the story

Read the transcription against the page, giving names, places, dates and numbers particular attention. A plausible but wrong surname can make two people look like one. A misread year can put a journey in the wrong decade. Keep uncertainty visible rather than allowing a smooth sentence to conceal a missing word.

The Smithsonian’s transcription guidance is a useful reference for the attitude to bring: retain the source’s wording and punctuation, and mark text that cannot be read instead of supplying a guess. That guidance is for its own projects, not a required Transkribus format; adopt the underlying discipline using the editor’s available uncertainty tags.[4][2]

Preserve the letter as written, including a nickname or unfamiliar spelling. Add a normalised name as separate information when the evidence supports it. This retains the writer’s voice while making it possible to connect several spellings of the same place. A corrected transcription and a modern paraphrase have different purposes.

Follow one thread through the collection

After correcting the sample, choose a question that the letters themselves might answer. Which places recur? When does a particular person first appear? How did the account of a journey change between its beginning and end? These questions give search a purpose beyond accumulating a pile of machine-readable text.

Transkribus lets you highlight words and apply textual tags for people, places, dates or custom categories, with attributes attached. Tags are managed across the collection. Use them to record a supported identification or an unresolved clue, then return to the surrounding passage when interpreting the result.[2]

Imagine that three letters mention a shop using a formal name, a shortened name and a family nickname. Once you establish the connection from the pages or a relative’s knowledge, a shared tag can gather those mentions. The interesting result is the sequence of accounts around that shop, not the tag itself. This is an illustrative case, not a discovery from a real archive.

Create a short chronological index from the corrected sample, keeping the evidence for each date beside it. Where a date is approximate, retain the approximation. A gap between surviving letters can mean missing correspondence; it does not establish what the writer was doing during that interval.

Make a copy the family can keep

The export documentation provides standard downloads including page images, Word documents and PDFs with embedded text. Export a small sample first and check what survived: page order, searchability, line breaks and any visible annotations. Keep the corrected transcription and structured index separately rather than assuming every textual tag will travel into a Word or PDF file.[3][2]

A searchable PDF is a satisfying reading copy because relatives can find a name and return to the actual page. A separate structured index—CSV, spreadsheet or database—should preserve normalised people, places, dates, uncertainty and the stable page reference. The two outputs serve different purposes: one is for reading; the other keeps the research relationships editable.[3][2]

Ask a relative to review a few passages with you. They may recognise a place that the model could only spell approximately, or explain why a familiar name appears in an unexpected form. Record those explanations as later notes, distinct from the letter’s words. That separation lets the collection grow richer without blurring evidence and recollection.

The resulting archive need not be large to feel transformative. Ten carefully prepared pages can turn “there was something about that summer” into a passage you can find, read and share. The technology helps the correspondence become accessible; the voice that makes it worth reading is still the person who wrote it.

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