Opportunities and Challenges of Handwritten Sanskrit Character Recognition System

The rapid growth in the field of internet facilities and digitalization, changes the living way of human being. Due to internet facilities and services, anyone can access data from anywhere. A lot of online data are generating day by day, so that data needs to be processed before extracting the information. Therefore the demand of Natural language Processing (NLP) Techniques has been increased. The Pattern recognition is sub-field of NLP. The field of Pattern Recognition is a branch of machine learning that contributed up to great extent in the Computer Vision and Machine Vision applications. Pattern Recognition is concerned with the recognition of patterns and regularities in data. Handwriting recognition is one of the challenging subtask and current research field under Pattern Recognition, due to different ways of writing and handwriting styles. Handwritten Sanskrit Characters recognition is more complicated than other languages works in online and offline mode, because Sanskrit characters have more consonants and modifiers. In this paper discussed the opportunities and challenges of Handwritten Sanskrit Character Recognition System

A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is difficult and becomes computationally intractable. Therefore, alternative soft decisions are needed at the pre-processing level. This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network. In this paper we construct a multi-hypotheses tree architecture with candidate segments of line sequences from different segmentation algorithms at its different branches. The deep neural network is trained on perfectly segmented data and tests each of the candidate segments, generating unicode sequences. In the verification step, these unicode sequences are validated using a sub-string match with the language model and best first search is used to find the best possible combination of alternative hypothesis from the tree structure. Thus the verification framework using language models eliminates wrong segmentation outputs and filters recognition errors