A Deep Learning-based Unified Solution for Character Recognition

Optical Character Recognition(OCR) has become a crucial area of research due to the vast number of digitized documents to lessen the dependency on paper. One can save time and money on data entry by automatically extracting information off paper and putting it where it needs to go. There has been much research on OCR systems for different languages, but a unified system that is agnostic to language does not exist. In this work, we propose a multi-headed resunet++ based solution that can recognize the low resource languages(Bangla, Assamese, etc.) and performs well on resource-rich languages(such as English, Arabic, etc.). The backbone of the solution, i.e., resunet++, is fundamentally designed for medical image segmentation that is very complex. As the low representative languages are mostly of cursive style and complex in nature, this backbone can help share those higher-level features and pass them to the lower level. Our proposed solution is applied to isolated characters of Bangla, Assamese, and English languages. For Bangla, the segmentation is done by our developed method, and the dataset was pre-segmented for the other two languages. Applying the solution, we achieved a satisfactory performance.