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.
