Progressive rectification network for irregular text recognition Scene text recognition has received increasing attention in the research community. Text in the wild often possesses irregular arrangements, which typically include perspective, curved, and oriented texts. Most of the existing methods do not work well for irregular text, especially for severely distorted text. In this paper, we propose a novel progressive rectification network (PRN) for irregular scene text recognition. Our PRN progressively rectifies the irregular text to a fronthorizontal view and further boosts the recognition performance. The distortions are removed step by step by leveraging the observation that the intermediate rectified result provides good guidance for subsequent higher quality rectification. Additionally, by decomposing the rectification process into multiple procedures, the difficulty of each step is considerably mitigated. First, we specifically perform a rough rectification, and then adopt iterative refinement to gradually achieve optimal rectification. Additionally, to avoid the boundary damage problem in direct iterations, we design an enveloperefinement structure to maintain the integrity of the text during the iterative process. Instead of the rectified images, the text line envelope is tracked and continually refined, which implicitly models the transformation information. Then, the original input image is consistently utilized for transformation based on the refined envelope. In this manner, the original character information is preserved until the final transformation. These designs lead to optimal rectification to boost the performance of succeeding recognition. Extensive experiments on eight challenging datasets demonstrate the superiority of our method, especially on irregular benchmarks.

Deep Network with PixelLevel Rectification and Robust Training for Handwriting Recognition Offline handwriting recognition is a wellknown 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 pixellevel rectification into a CNN and RNN based model. We also propose an adjacent output mixup method for RNN layeru0027s 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 pretraining, 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 IFNENIT datasets.