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The handwritten text recognition problem is widely studied by the researchersof computer vision community due to its scope of improvement and applicabilityto daily lives, It is a sub-domain of pattern recognition. Due to advancementof computational power of computers since last few decades neural networksbased systems heavily contributed towards providing the state-of-the-arthandwritten text recognizers. In the same direction, we have taken twostate-of-the art neural networks systems and merged the attention mechanismwith it. The attention technique has been widely used in the domain of neuralmachine translations and automatic speech recognition and now is beingimplemented in text recognition domain. In this study, we are able to achieve4.15% character error rate and 9.72% word error rate on IAM dataset, 7.07%character error rate and 16.14% word error rate on GW dataset after merging theattention and word beam search decoder with existing Flor et al. architecture.To analyse further, we have also used system similar to Shi et al. neuralnetwork system with greedy decoder and observed 23.27% improvement in charactererror rate from the base model.

Lexicon and Attention based Handwritten Text Recognition System