An Efficient Feature Vector for Segmentation-free Recognition of Online Cursive Handwriting Based on a Hybrid Deep Neural Network

It presents our recent study on segmentation-free recognition of unconstrained cursive online handwriting of Devanagari and Bangla, the two most popular Indic scripts. Here, we have devised an efficient algorithm for obtaining the core region of a handwritten word sample and compute a robust feature set which includes certain measures based on the knowledge of this core region. It also includes first and second order discrete derivatives which estimate certain geometric properties of discrete curves along the pen trajectory of the input online handwritten word sample. The proposed feature vector is obtained at each point on the trajectory of the preprocessed word sample based on a window (called vicinity) centered at the point. A hybrid deep neural network architecture consisting of a convolutional neural network (CNN), a bidirectional recurrent neural network (BiRNN) having Long Short Term Memory (LSTM) cells and a connectionist temporal classification (CTC) layer receives this high level feature vector as input for labelling the character sequence in the online word sample. This study shows that the proposed hybrid architecture recognizes online handwriting more efficiently than a BLSTM network alone.