Segmentation-Free Bangla Offline Handwriting Recognition Using Sequential Detection of Characters and Diacritics with a Faster R-CNN
This paper presents an offline handwriting recognition system for Bangla script using sequential detection of characters and diacritics with a Faster R-CNN. This is an entirely segmentation-free approach where the characters and associated diacritics are detected separately with different networks named C-Net and D-Net. Both of these networks were prepared with transfer learning from VGG-16. The essay scripts from the Boise State Bangla Handwriting Dataset along with standard data augmentation techniques were used for training and testing. The F1 scores for the C-Net and D-Net networks are 89.6% and 93.2% respectively. Afterwards, both of these detection modules were fused into a word recognition unit with CER (Character Error Rate) of 11.2% and WER (Word Error Rate) of 24.4%. A spell checker further minimized the errors to 8.9% and 21.5% respectively. This same method is likely to be equally effective on several other Abugida scripts similar to Bangla.
Majid, Nishatul and Barney Smith, Elisa H.. (2019). "Segmentation-Free Bangla Offline Handwriting Recognition Using Sequential Detection of Characters and Diacritics with a Faster R-CNN". Proceedings: The 15th IAPR International Conference on Document Analysis and Recognition, 228-233. https://dx.doi.org/10.1109/ICDAR.2019.00045