SegmentationFree Bangla Offline Handwriting Recognition using Sequential Detection of Characters and Diacritics with a Faster RCNN This paper presents an offline handwriting recognition system for Bangla script using sequential detection of characters and diacritics with a Faster RCNN. This is an entirely segmentationfree approach where the characters and associated diacritics are detected separately with different networks named CNet and DNet. Both of these networks were prepared with transfer learning from VGG16. 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 CNet and DNet 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.

Detecting Named Entities in Unstructured Bengali Manuscript Images In this paper, we undertake a task to find named entities directly from unstructured handwritten document images without any intermediate textcharacter recognition. Here, we do not receive any assistance from natural language processing. Therefore, it becomes more challenging to detect the named entities. We work on Bengali script which brings some additional hurdles due to its own unique script characteristics. Here, we propose a new deep neural networkbased architecture to extract the latent features from a text image. The embedding is then fed to a BLSTM (Bidirectional Long ShortTerm Memory) layer. After that, the attention mechanism is adapted to an approach for named entity detection. We perform experimentation on two publiclyavailable offline handwriting repositories containing 420 Bengali handwritten pages in total. The experimental outcome of our system is quite impressive as it attains 95.43% balanced accuracy on overall named entity detection.