Handwriting Recognition Guide

A skill for applying handwriting text recognition (HTR) to digitize historical documents, archival manuscripts, and handwritten research notes. Covers HTR platforms, image preprocessing, model training, post-correction, and integration into digital humanities research workflows.

Handwriting Recognition vs. Printed OCR

Key Differences

Printed Text OCR:
  - Characters are standardized and uniform
  - Well-solved problem (>99% accuracy on clean scans)
  - Tools: Tesseract, ABBYY FineReader, Adobe Acrobat

Handwriting Text Recognition (HTR):
  - Characters vary by writer, mood, pen, era
  - Much harder -- typically 85-95% character accuracy
  - Requires training on specific handwriting styles
  - Tools: Transkribus, Kraken, HTR-Flor, Google Cloud Vision