Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks

Understand

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.