ESIR: End-To-End Scene Text Recognition via Iterative Image Rectification

Abstract

Automated recognition of texts in scenes has been a research challenge for years, largely due to the arbitrary text appearance variation in perspective distortion, text line curvature, text styles and different types of imaging artifacts. The recent deep networks are capable of learning robust representations with respect to imaging artifacts and text style changes, but still face various problems while dealing with scene texts with perspective and curvature distortions. This paper presents an end-to-end trainable scene text recognition system (ESIR) that iteratively removes perspective distortion and text line curvature as driven by better scene text recognition performance. An innovative rectification network is developed, where a line-fitting transformation is designed to estimate the pose of text lines in scenes. Additionally, an iterative rectification framework is developed which corrects scene text distortions iteratively towards a fronto-parallel view. The ESIR is also robust to parameter initialization and easy to train, where the training needs only scene text images and word-level annotations as required by most scene text recognition systems. Extensive experiments over a number of public datasets show that the proposed ESIR is capable of rectifying scene text distortions accurately, achieving superior recognition performance for both normal scene text images and those suffering from perspective and curvature distortions.