Exploration of CNN Features for Online Handwriting Recognition

Recently, convolution neural network (CNN) has demonstrated its powerful ability in learning features particularly from image data. In this work, its capability of feature learning in online handwriting is explored, by constructing various CNN architectures. The developed CNNs can process online handwriting directly unlike the existing works that convert the online handwriting to an image to utilize the architecture. The first convolution layer accepts the sequence of (x; y) coordinates along the trace of the character as an input and outputs a convolved filtered signal. Thereafter, via alternating steps of convolution and Rectified Linear Unit layers, in a hierarchical fashion, we obtain a set of deep features that can be employed for classification. We utilize the proposed CNN features to develop a Support Vector Machine (SVM)-based character recognition system and an implicit-segmentation based large vocabulary word recognition system employing hidden Markov model (HMM) framework. To the best of our knowledge, this is the first work of its kind that applies CNN directly on the (x; y) coordinates of the online handwriting data. Experiments are carried out on two publicly available English online handwritten database: UNIPEN character and UNIPEN ICROW-03 word databases. The obtained results are promising over the reported works employing the point-based features.