A proposed deep learning approach for Bangla handwritten character recognition

Abstract

The present research on Bangla Handwritten Character Identification investigated a wide range of deep learning architectures, including DenseNet201, VGG19, MobileNetV2, ResNet101, CNN01, and CNN02, and evaluated their efficacy in identifying complicated Bangla characters. Among these models, DenseNet201 stood out as the best performer, with an outstanding 81.29% accuracy. This high level of precision demonstrates DenseNet201's ability to capture the complex features and variances found in Bangla characters, making it an excellent choice for real-world applications. The analysis revealed beneficial insights into each architecture's cultural value, giving light on their particular capabilities and limits in the particular assignment of Bangla Handwritten Text Identification.DenseNet201's popularity not only establishes it as an attractive choice, but also focused on its potential impact on informative, cultural, and access areas within the Bengali-speaking population. As we navigate the deep learning model landscape, this research not only provides a thorough review of alternative architectures, but also points to the essential relevance of picking the most accurate modeling. The success of DenseNet201 is a convincing example, demonstrating the importance of selecting the correct architecture for the effective deployment of Bangla Handwritten Character Identification systems. This research not only advances character identification technology, but also highlights the practical consequences of these technologies in a variety of societal contexts, maintaining the importance of precision and dependability in selecting models.