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.

This paper presents a comparative study of three feature extraction approaches for online handwritten word recognition of two major Indic scripts-Bengali and Devanagari using Hidden Markov Model (HMM). First approach uses feature extraction from whole stroke without local zone division after segmenting the word into its basic strokes. Whereas, other two approaches consider the segmentation of a word into its basic strokes and a local zone wise analysis of each online stroke. Among these two zone wise local features, one takes into account structural and directional features and other uses dominant points, detected from strokes using slope angles, to find the local features. These features are studied in HMM-based word recognition platform. From the comparative study of the word recognition results, we have noted that dominant point based local feature extraction provides best accuracies for both Bengali and Devanagari scripts. We have obtained 90.23% and 93.82% accuracies for Bengali and Devanagari scripts respectively.

This paper presents two zone-based feature extraction approaches for online handwritten character recognition of two major Indic scripts-Bengali and Devanagari. Here, each stroke of an online character is divided into a number of local zones. In the first approach, named Zone wise structural and directional features (ZSD), structural and directional features are extracted for each stroke in each of these local zones. In the second approach, named Zone wise slopes of dominant points (ZSDP), the dominant points are detected first from each stroke and next the slope angles between consecutive dominant points are calculated and features are extracted in these local zones. Next, these features are fed to SVM classifier for stroke recognition. The constituent stroke combinations of characters are matched with training data and characters are recognized accordingly. Using ZSD, the recognition performances for Bengali (9,800 test data) and Devanagari (10,000 test data) scripts are 87.48% and 85.10% and with ZSDP, the accuracies are 92.48% and 90.63% respectively.