Transformer based Urdu Handwritten Text Optical Character Reader
This work addresses the digitization of Urdu handwritten text, which is incremental as it applies an existing transformer method to a new, challenging language.
The authors tackled the problem of Urdu handwritten text recognition, a low-resource language with cursive script, by proposing a transformer-based optical character reader, achieving results that address the lack of existing work in this domain.
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.