Kurrent and Old German Script OCR: Our Accuracy on 17th-Century Records, Measured Honestly

Anyone researching German ancestry eventually runs into the wall of Kurrent, the old German script that filled church books, court records, and family letters until the 1940s. The handwriting is beautiful, its letterforms share almost nothing with modern cursive, and the number of people who can still read it fluently shrinks every year. A growing number of AI transcription tools, ours included, now claim to help with exactly this problem, and this post reports what actually happens when you measure one of them properly.

The stakes in genealogy are different from those of ordinary transcription, because misreading a word in a diary costs you a word, whereas misreading the surname in your great-great-grandmother's baptismal record and presenting that mistake confidently can cost you months of research into a family that was never yours. The accuracy that matters here is accuracy on names, so names are what we set out to measure.

The test set of sixteen documents with human-verified transcriptions

We assembled sixteen historical German documents, each paired with a transcription verified by a human being. There is no AI-generated ground truth anywhere in the set, because a benchmark scored against AI output would only tell us whether two models agree with each other.

• Six pages of 17th-century Kanzleikurrent come from the Dresdner Hofdiarium, a court diary kept between 1653 and 1673. The transcriptions are expert work published by SLUB Dresden under a CC BY 4.0 license, and the pages are dense with names, places, and titles.
• Eight postcard addresses written between roughly 1880 and 1940 in Kurrent script come from the CC BY-SA "Greetings From!" research dataset. Of everything publicly available, these are the closest match to the documents actually sitting in a family shoebox.
• One control document written in ordinary Latin cursive rounds out the set, along with two artificially degraded copies of other documents that simulate a bad phone photo.

Every document is annotated with the people and places it mentions, because character error rate alone hides what genealogists care about. We report three measurements: named-entity accuracy, meaning the fraction of people and places transcribed correctly; character error rate across all text; and silent errors, meaning names that came out wrong while being displayed at high confidence with no warning.

The results

The pattern hiding behind those averages matters as much as the averages themselves. On the postcard addresses, which are the closest match to typical family documents, most of the clean-ink cards scored two out of three or three out of three on names, and a few of them transcribed almost perfectly. On the 17th-century court hands we genuinely struggle, and there is no way to dress that up, because a 350-year-old chancery script sits far outside anything that modern AI vision models encountered in training.

The third row deserves a plain-language explanation, because it is the number we most wish were zero. Our verification layer catches and flags many wrong names, but on this material a meaningful share of wrong names still arrives looking confident. Measured directly, the confidence our system displays on Kurrent overstates itself roughly 44 to 48 percent of the time. On modern handwriting the same figure is about 16 percent, and you can read that full benchmark in our modern handwriting accuracy report. The gap between those two numbers is the honest summary of where AI handwriting recognition stands in 2026: dependable on the material it grew up on, and overconfident on the material your ancestors actually wrote.

A detailed account of where we fail

The most instructive failure in the whole set came from a faded pencil postcard, where the underlying vision model invented a complete, fluent, and entirely wrong address before our verification layer caught the problem and flagged the words. This tendency toward fluent invention appears in every general-purpose AI vision model we have tested, and it is the single best reason not to trust an unverified transcription of historical script from any single model, ours or anyone else's.

The rosters of rare 17th-century surnames were mostly beyond us, with names like Zackroffskg, Mezeradt, and Hartizsch defeating a pipeline that recovers the page structure and the common vocabulary well enough but cannot yet be relied upon for rare names in documents of that age. If your ancestor's name is unusual and the record dates from 1654, you should treat our output as a starting draft and nothing more.

The artificially degraded copies lost a great deal of accuracy compared to their clean originals. Whatever tool you use, photographing documents flat, in good light, and at full resolution will improve your results more than switching vendors will.

What we recommend for German records today

Used with clear eyes, PenParse is still genuinely useful on this material. It produces a draft in twenty seconds that would take a non-reader of Kurrent hours to attempt, it gets the document structure and much of the common vocabulary right, and on postcard-era handwriting it reads most names correctly. Our advice is to treat every name and place in the output as unverified until you have compared it against the original strokes, and to lean on the human communities that specialize in this script, such as the r/Kurrent community on Reddit, for the words that matter most to your research.

If Kurrent is the bulk of your research rather than an occasional document, this is the one case where we would point you at a specialist tool first. Transkribus has models trained specifically on historical German script, and a trained model on a consistent corpus will beat a general-purpose system on this material. We set out where that trade-off falls in our honest Transkribus comparison, including what its free tier covers.

The next version, already measured in the lab

Two changes are coming, and both of them will be published against this same benchmark. The first concerns honesty at the interface, in that we are adding script detection, so that when you upload a document written in Kurrent or a similar historical hand, PenParse will tell you plainly that its confidence scores on this material are less reliable, rather than decorating uncertain names with reassuring numbers. The second change concerns accuracy itself, and in laboratory testing on this same benchmark, a hybrid pipeline that pairs an open-source recognition model specializing in historical German script with an AI correction pass reaches 65 to 70 percent named-entity accuracy at roughly 11 percent character error rate, which is roughly double our current production accuracy on the hard material. When it ships, we will rerun this exact benchmark and publish the new numbers, so the improvement is something you can inspect rather than something you have to take on faith.

Three numbers every vendor should be able to give you

If you are evaluating any transcription tool for historical documents, ask the vendor for three numbers: accuracy on named entities specifically, measured against human-verified ground truth, together with a stated rate of confident-but-wrong output. A vendor who cannot produce those three numbers has not measured the thing that matters for your research.

Our evaluation set is built entirely from CC-licensed public sources and the methodology is documented. If you build or sell a handwriting transcription tool and would like to run it, get in touch, and we will compare notes publicly.

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You can try it on your own documents: PenParse transcribes three pages free with no signup, and it shows you exactly which words it is not sure about.

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