Scopus Indexed Paper

Paper Details


Title
EkushNet: Using Convolutional Neural Network for Bangla Handwritten Recognition
Abstract
EkushNet is the first research which can recognize Bangla handwritten basic characters, digits, modifiers, and compound characters. Handwritten recognition is one of the most interesting issue in present time due to its variant applications and help to make the old form and information digitization and reliable. In spite of, there is no single model which can classify all types of Bangla characters. One of most common reason conducting with handwritten scripts is big challenge because of every person has unique style to write and also has different shape and size. Therefore, EkushNet is proposed a model which help to recognize Bangla handwritten 50 basic characters, 10 digits, 10 modifiers and 52 mostly used compound characters. The proposed model train and validate with Ekush [1] dataset and cross-validated with CMATERdb [2] dataset. The proposed method is shown satisfactory recognition accuracy 97.73% for Ekush dataset, and 95.01% cross-validation accuracy on CMATERdb dataset, which is so far, the best accuracy for Bangla character recognition.
Keywords
Bangla handwritten, Data Science, Machine Learning, Deep Learning, Computer Vision, Patteran Recognition
Authors
AKM Shahariar Azad Rabby, Sadeka Haque, Sheikh Abujar, Syed Akhter Hossain
Phone
Journal or Conference Name
Procedia Computer Science
Publish Year
2018
Indexing
Scopus