Journal Article

Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks

Hasan, Mahmudul, Alom, Md Zahangir, Taha, Tarek M., Asari, Vijayan K., Sidike, Paheding

2018

Overview

In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object distortions. The experimental results show the superior performance of DCNN models compared with the other popular object recognition approaches, which implies DCNN can be a good candidate for building an automatic HBCR system for practical applications.

• Publisher: Hindawi Publishing Corporation,Hindawi,John Wiley & Sons, Inc
• Subject: Accuracy / Artificial intelligence / Artificial neural networks / Character recognition / Classification / Computational linguistics / Data processing / Feature extraction / Handwriting / Handwriting recognition / Humans / Image Processing, Computer-Assisted - methods / International conferences / Language / Language processing / Machine Learning / Natural language interfaces / Neural networks / Neural Networks (Computer) / Object recognition / Office automation / Pattern recognition / Pattern Recognition, Automated - methods / Remote sensing / State of the art