  In this work, we jointly address the problem of text detection and
recognition in natural scene images based on convolutional recurrent neural
networks. We propose a unified network that simultaneously localizes and
recognizes text with a single forward pass, avoiding intermediate processes
like image cropping and feature re-calculation, word separation, or character
grouping. In contrast to existing approaches that consider text detection and
recognition as two distinct tasks and tackle them one by one, the proposed
framework settles these two tasks concurrently. The whole framework can be
trained end-to-end, requiring only images, the ground-truth bounding boxes and
text labels. Through end-to-end training, the learned features can be more
informative, which improves the overall performance. The convolutional features
are calculated only once and shared by both detection and recognition, which
saves processing time. Our proposed method has achieved competitive performance
on several benchmark datasets.
