HistoriaMP

From manuscript image to verifiable reading.

HistoriaMP examines historical handwriting with — source-bound, traceable and with visible .

No conventional OCR output: the platform connects , , abbreviations and quality control into a justified .

View demo Open glossary kontakt@historiamp.com
Visualization of source-bound manuscript analysis with visible evidence, uncertainty and justified reading
How it works

Evidence first, reading later.

01

Check the image basis

Image version, crop and visible areas are documented before a is created.

02

Make evidence visible

Layout, segments, , abbreviations and critical areas remain verifiable as .

03

Justify the reading

Uncertain areas are marked. A reading is not smoothed, but traced back to visual evidence.

Simplified demonstration

The following simulation is a deliberately simplified model. It does not show the complete real pipeline, but the basic principle: source image, module steps, artifacts, and quality control remain visible and verifiable.

DEMONSTRATION VIEW — PRE-MARKED IMAGE SEQUENCE
HMP_DEMO_MS131_0001
IMAGE SEQUENCE / MS131_6 READY
Original manuscript image M01 source frame M02 layout zones M03 segment grid M04 glyph markers M04A minim clusters M04B abbreviation candidates M05A reading layer M07 audit package
Source: The source image remains the primary source. The markings are prepared demonstration images.
RUN: HMP_DEMO_MS131_0001 READY
result_raw.jsonimmutable model output
result_approved.jsonhuman-reviewed layer
uncertainty_report.jsonpreserved ambiguity
quality_report.jsonpipeline audit state
Methodological notes

Blog

Texts on AI-assisted image analysis, source binding, documentation, uncertainty and digital manuscript workflows.

9 May 2026 HTR and LLMs in manuscript analysis: why the black box is not enough Why automatic text recognition and large language models are methodologically insufficient without visible evidence. 2 May 2026 When small signs raise big questions Why historical manuscripts need more than automatic text recognition. 30 April 2026 When the model input is not the source Why image scaling in AI analyses must be documented.
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Project structure

The most important project pages

These pages explain HistoriaMP’s method, pipeline, limits and long-term portal concept.

Method

Why HistoriaMP separates observation, analysis, reading and presentation.

Pipeline

How the path from image to verifiable reading is divided into modules and artifacts.

Limits

Which problems in LLM-assisted handwriting analysis are deliberately documented.

Portal

How HistoriaMP is conceived as a long-term research and access portal.

FAQ

Frequently asked questions about digital manuscript analysis

What is digital manuscript analysis?

Digital examines historical handwriting with digital methods. What matters is not only the text output, but the traceable connection to the visible .

Is HistoriaMP an OCR tool?

No. HistoriaMP does not treat manuscripts as a simple OCR task. The platform documents analytical layers such as , layout, segment, , uncertainty and reading.

Why is uncertainty important?

Uncertainty shows where a reading is not fully secured by the image evidence. This makes the analysis more verifiable and scientifically more robust.

What role do MUFI and Unicode play?

and help with the digital encoding of historical characters. They do not replace analysis of the visible manuscript form.