RNN Based Online Handwritten Word Recognition in Devanagari Script
Pooja Keshri, Prabhat Kumar, Rajib Ghosh · 2018
Devanagari script is the most popular script in India. But, very little recognized works have been done in this script towards development of online handwritten text recognition systems. The existence of large number of symbols and symbol order variations in this script, has led to low recognition rates for even the best existing recognition system. Most of the existing studies in Devanagari script have relied upon the same Hidden Markov Model (HMM) which has been used for so many years in handwriting recognition, despite of its familiar shortcomings. This article proposes a novel approach for online handwritten word recognition in Devanagari script based on two recently developed models of Recurrent Neural Network (RNN), termed as Long-Short Term Memory (LSTM) and Bidirectional Long-Short Term Memory (BLSTM), specifically designed for sequential data where the segmentation of data into basic unit level is very difficult. Analysis shows that words are written in non-cursive fashion in Devanagari script. The proposed approach considers the local zone wise analysis of each basic stroke of a word to extract various features from each basic stroke. In this local zone wise feature extraction approach, dominant points are detected from strokes using slope angles, to find the local features. These features are then studied using both LSTM and BLSTM versions of RNN. Most of the existing word recognition systems in this script have followed the typical holistic approach whereas the proposed system has been developed in analytical scheme with a total of 10K words in lexicon. An exhaustive experiment on large datasets has been performed to evaluate the performance of the proposed recognition approach using both LSTM and BLSTM to make a comparative performance analysis. Experimental results show that the proposed system outperforms existing HMM based systems in the literature.