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.

ICDAR2019 Competition on Recognition of Early Indian Printed Documents xe2x80x93 REID2019 This paper presents an objective comparative evaluation of page analysis and recognition methods for historical documents with text mainly in Bengali language and script. It describes the competition rules, dataset, and evaluation methodology. Results are presented for five methods  three submitted, one rerun, and one open source stateoftheart system. The focus is on optical character recognition (OCR) performance. Different evaluation metrics were used to gain an insight into the algorithms, including new character accuracy metrics to better reflect the difficult circumstances presented by the documents. The results indicate that deep learning approaches are promising, but there are still significant challenges for historic material of this nature.