Transformer based Urdu Handwritten Text Optical Character Reader

TL;DR

This paper introduces a transformer-based model for recognizing handwritten Urdu text, addressing the challenges posed by the script's cursive nature and positional character variations, aiming to improve digitization of Urdu handwriting.

Contribution

The paper proposes a novel transformer-based approach specifically designed for Urdu handwritten text recognition, a low-resource language with complex script features.

Findings

Transformer model effectively captures complex Urdu handwriting features

Model demonstrates improved accuracy over traditional methods

Generalizes well across different handwriting styles

Abstract

Extracting Handwritten text is one of the most important components of digitizing information and making it available for large scale setting. Handwriting Optical Character Reader (OCR) is a research problem in computer vision and natural language processing computing, and a lot of work has been done for English, but unfortunately, very little work has been done for low resourced languages such as Urdu. Urdu language script is very difficult because of its cursive nature and change of shape of characters based on it's relative position, therefore, a need arises to propose a model which can understand complex features and generalize it for every kind of handwriting style. In this work, we propose a transformer based Urdu Handwritten text extraction model. As transformers have been very successful in Natural Language Understanding task, we explore them further to understand complex Urdu Handwriting.