CBD2023: A Hypercomplex Bangla Handwriting Character Recognition Data for Hierarchical Class Expansion

Object recognition technology has made significant strides, but recognizing handwritten Bangla characters (including symbols, compound forms, etc.) remains a challenging problem due to the prevalence of cursive writing and many ambiguous characters. The complexity and variability of the Bangla script and individual's unique handwriting styles make it difficult to achieve satisfactory performance for practical applications, and the best existing recognizers are far less effective than those developed for English alpha-numeric characters. Compared to other major languages, there are limited options for recognizing handwritten Bangla characters. This research has described a new dataset to improve the accuracy and effectiveness of handwriting recognition systems for the Bengali language spoken by over 200 million people worldwide. This dataset aims to investigate and recognize Bangla handwritten characters, focusing on enlarging the recognized character classes. To achieve this, a new challenging dataset for handwriting recognition is introduced, collected from numerous students' handwriting from two institutions.