A Hybrid Model for End to End Online Handwriting Recognition

Automatic recognition of online handwritten words in a generic mode has significant application potentials. However, this recognition job is challenging for unconstrained handwriting data. The challenge is more serious for Indic scripts like Devanagari or Bangla due to the inherent cursiveness of their characters, large sizes of respective alphabets, existence of several groups of shape similar characters etc. On the other hand, with the recent development of powerful machine learning tools, major research initiatives in this area of pattern recognition studies have been observed. Feature extraction and classification are two major modules of such a recognizer. Deep architectures of convolutional neural network (CNN) models have been found to be efficient in extraction of useful features from raw signal. On the other hand, a recurrent neural network (RNN) along with connectionist temporal classification (CTC) has been shown to be able to label unsegmented sequence data. In the present article, we propose a hybrid layered architecture consisting of three networks CNN, RNN and CTC for recognition of online handwriting without use of any specific lexicon. In this study, we have also observed that feeding hand-crafted features to the CNN at the first level of the proposed model provides better performance than feeding the raw signal to the CNN. We have simulated the proposed model on two large databases of Devanagari and Bangla online unconstrained handwritten words. The recognition accuracies provided by the proposed model are encouraging.