Joint training of conditional random fields and neural networks for stroke classification in online handwritten documents

Abstract

The task of text/non-text stroke classification in online handwritten documents is an essential preprocessing step in document analysis. It is also a challenging problem since in many cases local features are not enough to generate high accuracy results and contextual information, such as temporal information and spatial information, must be carefully considered. In this paper, we propose a novel method, which jointly trains a combined model of conditional random fields and neural networks, to solve this problem. Both our unary and pairwise potentials are formulated as neural networks. The parameters of conditional random fields and neural networks are learned together during the training process. With much fewer parameters and faster speed, our method achieves impressive performance on the IAMonDo database, a publicly available database of freely handwritten documents.