RNN based online handwritten word recognition in Devanagari and Bengali scripts using horizontal zoning. (August 2019) - Ghosh, Rajib; Vamshi, Chirumavila; Kumar, Prabhat
Highlights: This article proposes a novel approach for online handwritten cursive and non-cursive word recognition in two of the most popular Indian scripts— Devanagari and Bengali, based on two recently developed versions of Recurrent Neural Network (RNN), named as Long–Short Term Memory (LSTM) and Bidirectional Long–Short Term Memory (BLSTM). The proposed approach divides each word horizontally into three zones— upper, middle, and lower, before carrying out training of basic strokes using LSTM and BLSTM versions of RNN. This type of zone division is done to reduce the variations in temporal orders of basic strokes within a word. The major strength of the proposed system is unlike most of the existing wordrecognition systems in these two scripts, it can recognize those words also which are not present in the trainingdataset as it considers basic stroke based class labelling scheme to train the classifier. The proposed system also overcomes various drawbacks of HMM that are common in existing HMM based word recognition systems. The experiments have been carried out in HMM based platform also to show the comparative performance analysis of the present system in both HMM and RNN based platforms. Experimental results show that the proposed zone segmentation technique and adopting LSTM–BLSTM based learning outperform existing word recognition systems including HMM based ones in these two Indian scripts. Abstract: Devanagari and Bengali scripts are two of the most popular scripts in India. Most of the existing word recognition studies in these two scripts have relied upon the widely used Hidden Markov Model (HMM), in spite of its familiar shortcomings. The existing works were evaluated against and performed well in their chosen metrics. But, the existing word recognition systems in these two scripts could not achieve more than 90% recognition accuracy. This article proposes a novel approach for online handwritten cursive and non-cursive word recognition in Devanagari and Bengali scripts based on two recently developed models of Recurrent Neural Network (RNN)—Long–Short Term Memory (LSTM) and Bidirectional Long–Short Term Memory (BLSTM). The proposed approach divides each word horizontally into three zones—upper, middle, and lower, to reduce the variations in basic stroke order within a word. Next, the word portions from middle zone are re-segmented into its basic strokes. Various structural and directional features are then extracted from each basic stroke of the word separately for each zone. These zone wise basic stroke features are then studied using both LSTM and BLSTM versions of RNN. Most of the existing word recognition systems in these two scripts have followed word based class labelling approach, whereas proposed system has followed the basic stroke based class labelling approach. An exhaustive experiment on large datasets has been performed to evaluate the performance of the proposed approach using both RNN and HMM to make a comparative performance analysis. Experimental results show that the proposed RNN based system is superior over HMM achieving 99.50% and 95.24% accuracies in Devanagari and Bengali scripts respectively and outperforms existing HMM based systems in the literature as well.