Research & R&D

Handwriting OCR is an AI service that turns handwritten documents into accurate, searchable, editable text. Unlike traditional OCR, which was built for printed text, it is built specifically for handwriting, including cursive, faded ink, historical scripts, and many languages, and it can translate non-English records into English in the same step. Upload a scan or photo and you get back clean text you can search, edit, and export to Word, Markdown, or plain text.

Handwriting OCR was founded in London in 2023, dedicated to applying modern AI to read the hardest handwritten documents: the cursive, faded, and historical pages that traditional OCR cannot handle. We are a small, independent UK team, the people who build the product also handle support, and we never use your documents to train models or share them with anyone.

Handwriting OCR processes laboratory notebooks including those with mixed text and technical notation. Mathematical equations are returned as LaTeX you can paste straight into a paper, and standard text, procedures, observations, and simple notation such as H₂O or NaCl typically process well. Complex chemical structures, reaction mechanisms, and isolated formulas may need verification. The technology works best when technical content is integrated with descriptive text rather than presented in isolation. Testing with sample pages from your notebooks will show how it handles your particular mix of content.

Yes. Many researchers use handwriting OCR specifically to digitize field notebooks into searchable databases. By processing field notes into text, you can search across years of observations for specific locations, phenomena, species, or conditions. The extracted text can be organized chronologically, thematically, or by location, and exports work with standard research tools and analysis software. This is particularly valuable for long-term ecological, archaeological, or anthropological work with accumulated handwritten observations.

Handwriting OCR processes multilingual documents that mix modern languages with Latin, Greek, or other languages common in research materials. It reads text based on what is actually written rather than expecting a single language throughout, so notes that combine languages, or text with specialized terminology, can all be processed, and non-English sources can be translated into English in the same step. Accuracy on unfamiliar segments depends on handwriting clarity and script complexity.

Yes. Handwriting OCR handles mixed-content pages where printed text and handwriting appear together, such as printed protocols with handwritten modifications, recording forms with handwritten entries, or photographed pages with annotations. The system recognizes both content types and preserves document structure, so you can see which portions came from print and which were added by hand.

No. Your research documents remain private. They are processed only to deliver results to you and are not used to train AI models, not shared with third parties, and not retained longer than necessary to complete processing. This matters for unpublished research, sensitive archival materials, and proprietary R&D data. Privacy and confidentiality are built into the service design as fundamental principles, not optional features.

Handwriting OCR processes scanned PDFs and common image formats including JPG, PNG, and TIFF, whether from a flatbed scanner or a smartphone photo. No conversion or special equipment is required, though scanning dense tables and faint pencil at 300 DPI or higher improves results. Output can be downloaded as editable text in Word (DOCX), Markdown, or plain text to fit your research and writing workflow.

Yes. You can process whole collections rather than individual documents, and the API supports automating bulk workflows. For very large projects, splitting sources into manageable PDFs and using the API keeps things efficient, and a managed processing option is available where you hand us the files and we return the results with the configuration tuned to your material. This suits archive-scale projects running into many thousands of pages.