Borno: Bangla Handwritten Character Recognition Using a Multiclass Convolutional Neural Network

Abstract

Handwriting recognition is still not a solved problem. With the advancements in artificial intelligence and machine learning, the construction of Optical Character Recognition systems (OCRs) has become more effective. However, there is still no serious commercially available OCRs for many low-resource languages, such as Bangla. Bangla presents additional challenges, since oftentimes, the vowels and consonants in the middle of the words are abbreviated and replaced with notations called diacritics, and multiple letters can be combined to build shorthand representations, called compound characters. Furthermore, the compound characters can have diacritics as well, making the recognition task extremely complex. This means that a successful commercial OCR should not only model individual characters but also model these diacritics and combined characters, leading us to propose a grapheme-based holistic recognition approach. Borno is the first multiclass convolutional neural network-based deep learning model that can recognize Bangla handwritten characters with graphemes. The proposed model has been trained on a dataset of 1,069,132 images, with 50 basic characters, 10 numerals, 146 compound characters, 10 modifiers, and 6 consonant diacritics classes. The trained Borno model achieves a 92.61% average character recognition accuracy in the validation set.