Borno: Bangla Handwritten Character Recognition Using a Multiclass Convolutional Neural Network
A K M Shahariar Azad Rabby, Md. Majedul Islam, Nazmul Hasan, Jebun Nahar, Fuad Rahman
Accepted to be presented at FTC 2020 - Future Technologies Conference 2020, 5-6 November 2020, Vancouver, Canada
Description
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 lowresource 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
