Text/Non-text Classification in Online Handwritten Documents with Recurrent Neural Networks

In this paper, we propose a novel method for text/non-text classification in online handwritten document based on Recurrent Neural Network (RNN) and its improved version, Long Short-Term Memory (LSTM) network. The task of classifying strokes in a digital ink document into two classes (text and non-text) can be seen as a sequence labelling task. The bidirectional architecture is used in these networks to access to the complete global context of the sequence being classified. Moreover, a simple but effective model is adopted for the temporal local context of adjacent strokes. By integrating local context and global context, the classification accuracy is improved. In our experiments on the Japanese ink documents (Kondate database), the proposed method achieves a classification rate of 98.75%, which is significantly higher than the 96.61% in the previous work. Similarly, on the English ink documents (IAMonDo database), it produces a classification rate of 97.68%, which is also higher than other results reported in the literature.