CrossModal Prototype Learning for ZeroShot Handwriting Recognition In contrast to machine recognizers that rely on training with large handwriting data, humans can recognize handwriting accurately on learning from few samples, and can even generalize to handwritten characters from printed samples. Simulating this ability in machine recognition is important to alleviate the burden of labeling large handwriting data, especially for large category set as in Chinese text. In this paper, inspired by human learning, we propose a crossmodal prototype learning (CMPL) method for zeroshot online handwritten character recognition: for unseen categories, handwritten characters can be recognized without learning from handwritten samples, but instead from printed characters. Particularly, the printed characters (one for each class) are embedded into a convolutional neural network (CNN) feature space to obtain prototypes representing each class, while the online handwriting trajectories are embedded with a recurrent neural network (RNN). Via crossmodal joint learning, handwritten characters can be recognized according to the printed prototypes. For unseen categories, handwritten characters can be recognized by only feeding a printed sample per category. Experiments on a benchmark Chinese handwriting database have shown the effectiveness and potential of the proposed method for zeroshot handwriting recognition.

Zero Shot Learning Based Script Identification in the Wild The text recognition system for natural images or video frames containing multilingual text needs a method to first identify the written script and then recognize the word in the identified script. However, the occurrence of some scripts is rare as compared to others. Due to the availability of a few samples of the rare script, the supervised learning of the deep neural networks is difficult. To overcome this problem, we have proposed a zeroshot learning based method for script identification. We have also proposed architecture for script identification which fuses the global feature vector and the semantic embedding vector. The semantic embedding of the script is obtained by using the spatial dependency of the strokeu0027s sequence via the recurrent neural network. The proposed architecture shows superior results as compared to the baseline approaches.