Deep Network with Pixel-Level Rectification and Robust Training for Handwriting Recognition

摘要

Offline handwriting recognition is a well-known challenging task in the optical character recognition (OCR) field due to the difficulty caused by various unconstraint handwriting styles. In order to learn invariant feature representations for handwriting, we propose a novel method to incorporate pixel-level rectification into a CNN and RNN based model. We also propose an adjacent output mixup method for RNN layer's training to improve the generalization ability of the model, i.e., the previous output of an RNN layer is added to the current output with random weights. We additionally adopt a series of techniques including pre-training, data augmentation and language model, and further analyze their contributions to the improvement of the model performance. The proposed method performs well on three public benchmarks, including the IAM, Rimes and IFN/ENIT datasets.