What We Do

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 the best existing recognizers do not lead to satisfactory performance for practical applications related to Bangla character recognition and have much lower performance than those developed for English alpha-numeric characters. To improve the performance of HBCR, we herein present the application of the state-of-the-art Deep Convolutional Neural Networks (DCNN) including VGG Network, All Convolution Network (All-Conv Net), Network in Network (NiN), Residual Network, FractalNet, and DenseNet for HBCR. The deep learning approaches have the advantage of extracting and using feature information, improving the recognition of 2D shapes with a high degree of invariance to translation, scaling and other distortions.

Why We Do it

With the growing pace of technology accuracy and time has become an untold demand of our daily life. When it comes to complex tasks like OCR for Bangla Handwriting, it becomes more hazardous regarding both of the issue mentioned before. To get a better experience with an excellent accuracy in "OCR for Bangla Handwriting", we're working on it using the Convolutional Neural Networks.