Bengali Handwritten Digit Recognition using CNN with Explainable AI
December 18, 2022·
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0 min read
MD Tanvir Rouf Shawon
Raihan Tanvir
Md. Golam Rabiul Alam
Abstract
Handwritten character recognition is a hot topic for research now-a-days. If we
can convert a handwritten piece of paper into a text-searchable document using the
Optical Character Recognition (OCR) technique, we can easily understand the
content and do not need to read a handwritten document. OCR in the English
language is very common, but in the Bengali language, it is very hard to find a
good quality OCR application. If we can merge machine learning and deep learning
with OCR, it can be a huge contribution to this field. A variety of techniques
have been suggested by various researchers on Bengali handwritten character
recognition. A lot of machine learning algorithms and deep neural networks were
used in their work, but the explanations of their models are not available. In our
work, we have used different machine learning models and CNN also to recognize
handwritten Bengali digits. We have got acceptable accuracy from some ML models,
and CNN has given us great testing accuracy. Grad-CAM was used as an XAI method
on our CNN model, which gave us insights about the model and helped us to detect
the origin of interest for recognizing a digit from an image.
Type
Publication
2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI 2022)

Authors
Senior Lecturer
I am Raihan Tanvir, currently serving as a Senior Lecturer in the Department of Computer Science and Engineering at Ahsanullah University of Science and Technology (AUST) in Dhaka, Bangladesh. My research spans Computer Vision, Natural Language Processing (NLP), Large Language Models (LLMs), Vision-Language Models (VLMs), and multimodal deep learning.