Handwritten Text Recognition using Deep Learning and Word Beam Search

This paper offers a solution to traditional handwriting recognition techniques using concepts of Deep learning and Word Beam Search. This paper explains about how an individual handwritten word is classified from the handwritten text by translating into a digital form. The digital form when trained with the Connectionist Temporal Classification (CTC) loss function, the output produced is a RNN. This is a matrix containing character probabilities for each time-step. The final text is mapped using a CTC decoding algorithm by converting the character probabilities. The recognized text is constructed by a list of words from the dictionary by using the token passing algorithm. It is found the running time of token passing depends on the size of dictionary. Also the numbers like arbitrary character strings will not able to decode. In this paper the decoding search algorithm word beam search is proposed, in order to tackle these types of problems. This methodology support to constrain words similar to those contained in a dictionary. It allows the character strings such as arbitrary non-word between the words, and integrates into a word-level language model. It is found the running time is better when compared with the token passing. The proposed algorithm comprises of the decoding algorithm named vanilla beam search and token passing using the IAM dataset and Bentham data set.