Abstract:
The difficulty of handwritten character identification varies by language, owing to
differences in shapes, lines, numbers, and size of characters. There are several studies for
the identification of handwritten characters accessible for English in comparison with other
significant languages like Bangla. In their recognition procedures, existing technologies
use multiple techniques such as classification tools and feature extraction. CNN has
recently been shown to be proficient in handwritten character recognition in English. A
Handwritten Bangla character identification system based on CNN has been examined in
this research. Using CNN, the suggested approach for feature, labeling, and normalizing
the handwritten character of images, as well as categorizing different characters. It doesn't
use a feature extraction approach like previous research in the field. This research used
almost 4,50,000 unique handwritten characters in a variety of styles. The recommended
model has been proved to have a high recognition accuracy level and outperforms some of
the most widely used methods already in use.
