Development of a Multi-User Recognition Engine for Handwritten Bangla Basic Characters and Digits

TL;DR

This paper presents a multi-user handwritten Bangla character recognition system using Tesseract OCR, achieving over 90% accuracy by training user-specific models on collected handwritten samples.

Contribution

It introduces a method for creating user-specific OCR models for handwritten Bangla characters, demonstrating high recognition accuracy with a publicly available OCR engine.

Findings

User-specific models achieved up to 96.87% accuracy.

Overall character recognition accuracy was 92.15%.

System had segmentation failure rates of around 12-16%.

Abstract

The objective of the paper is to recognize handwritten samples of basic Bangla characters using Tesseract open source Optical Character Recognition (OCR) engine under Apache License 2.0. Handwritten data samples containing isolated Bangla basic characters and digits were collected from different users. Tesseract is trained with user-specific data samples of document pages to generate separate user-models representing a unique language-set. Each such language-set recognizes isolated basic Bangla handwritten test samples collected from the designated users. On a three user model, the system is trained with 919, 928 and 648 isolated handwritten character and digit samples and the performance is tested on 1527, 14116 and 1279 character and digit samples, collected form the test datasets of the three users respectively. The user specific character/digit recognition accuracies were obtained as 90.66%, 91.66% and 96.87% respectively. The overall basic character-level and digit level accuracy of the system is observed as 92.15% and 97.37%. The system fails to segment 12.33% characters and 15.96% digits and also erroneously classifies 7.85% characters and 2.63% on the overall dataset.