Advancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer Learning

As deep learning continues to evolve, we have observed huge breakthroughs in the fields of medical imaging, video and frame generation, optical character recognition (OCR), and other domains. In the field of data analysis and document processing, the recognition of handwritten numerals plays a crucial role. This work has led to remarkable changes in OCR, historical handwritten document analysis, and postal automation. In this study, we present a novel framework to overcome this challenge, going beyond digit recognition in only one language. Unlike common methods that focus on a limited set of languages, our method provides a comprehensive solution for recognition of handwritten digit images in 12 different languages. These specific languages are chosen because most of them have fairly distant representations in latent space. We utilize transfer learning, as it reduces the computational cost and maintains the quality of enhanced images and the models’ recognition accuracy. Another strength of our approach is the innovative attention-based module called the MRA module. Our experiments confirm that by applying this module, major progress is made in both image quality and the accuracy of handwritten digit recognition. Notably, we reached high precisions, surpassing nearly 2% improvement in specific languages compared to earlier techniques. In this work, we present a robust and cost-effective approach that handles multilingual handwritten numeral recognition across a wide range of languages. The code and further implementation details are available at https://github.com/CVLab-SHUT/HandWrittenDigitRecognition.