Unconstrained Offline Handwritten Word Recognition by Position Embedding Integrated ResNets Model

Abstract

The state-of-the-art methods usually integrate with linguistic knowledge in the recognizer, which makes models more complicated and hard for resource-lacking languages. This letter proposes a new method for unconstrained offline handwritten word recognition by combining position embeddings with residual networks (ResNets) and bidirectional long short-term memory (BiLSTM) networks. At first, ResNets are used to extract abundant features from the input image. Then, position embeddings are used as indices of the character sequence corresponding to a word. By combining the ResNets features with each position embedding, the model generates different inputs for the BiLSTM networks. Finally, the state sequence of the BiLSTM is used to recognize corresponding characters. Without additional language resource, the proposed model achieved the best result on two public corpora, i.e., the 2017 ICDAR word-level information extraction in historical handwritten records competition and the RIMES public dataset on character error rate.