Development of a multi-user handwriting recognition system using Tesseract open source OCR engine

TL;DR

This paper presents a multi-user handwritten Roman script recognition system using the open-source Tesseract OCR engine, trained with user-specific data to improve accuracy for individual handwriting styles.

Contribution

It introduces a method to create user-specific models in Tesseract for recognizing handwritten text, achieving notable accuracy improvements for individual users.

Findings

User-specific models achieved up to 87.92% accuracy.

Overall system accuracy was 78.39%.

Character segmentation failure rate was 10.96%.

Abstract

The objective of the paper is to recognize handwritten samples of lower case Roman script using Tesseract open source Optical Character Recognition (OCR) engine under Apache License 2.0. Handwritten data samples containing isolated and free-flow text were collected from different users. Tesseract is trained with user-specific data samples of both the categories of document pages to generate separate user-models representing a unique language-set. Each such language-set recognizes isolated and free-flow handwritten test samples collected from the designated user. On a three user model, the system is trained with 1844, 1535 and 1113 isolated handwritten character samples collected from three different users and the performance is tested on 1133, 1186 and 1204 character samples, collected form the test sets of the three users respectively. The user specific character level accuracies were obtained as 87.92%, 81.53% and 65.71% respectively. The overall character-level accuracy of the system is observed as 78.39%. The system fails to segment 10.96% characters and erroneously classifies 10.65% characters on the overall dataset.