Scene text recognition with high performance CNN classifier and efficient word inference

The recognition of text in natural scene images is a practical yet challenging task due to the large variations in backgrounds, textures, fonts, and illumination conditions. In this paper, we propose a highly accurate character recognition model by utilizing the representational power of a specially designed Convolutional Neural Network (CNN). Based on the recognition model, we also develop an efficient post processing approach for error correction and hypothesis re-verification. Character and word image recognition experiments on two public datasets, namely the ICDAR 2003 Robust Reading dataset and the Street View Text (SVT) dataset both show that the proposed approach provides superior or comparable results to the state-of-the-art techniques.