Hierarchical Recurrent Neural Network for Handwritten Strokes Classification

The paper presents an original solution to the online handwritten document processing in a free form, which is aimed at separating multi-class handwritten documents into texts, tables, formulas, drawings, etc. Stroke classification is an important step in automatic document layout analysis (DLA) in handwritten document recognition systems. Major DLA challenges arise due to a wide diversity of handwritten content, various writing styles, a lack of contextual knowledge, and the complicated structure of freeform handwritten documents. In this paper, we propose the hierarchical recurrent neural network (RNN) architecture to address the hierarchical structure inherent to the handwritten document. The novelty of feature aggregation pooling technique for transferring data between hierarchical levels allows achieving higher computational efficiency for using the suggested approach in on-device mobile computing. The presented approach gives an access to new state-of-the-art results in the task of multi-class classification with an accuracy of 97.25% on the IAMonDo dataset. This result can serve as the basis for efficient mobile applications for freeform handwriting document recognition.