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

TL;DR

This paper introduces a unified convolutional recurrent neural network that jointly detects and recognizes text in natural scene images in a single, end-to-end trainable framework, improving efficiency and performance.

Contribution

The paper presents a novel end-to-end trainable network that simultaneously localizes and recognizes text, avoiding intermediate steps and sharing features for efficiency.

Findings

Achieved competitive results on benchmark datasets.

Reduced processing time by sharing convolutional features.

Demonstrated improved performance through end-to-end training.

Abstract

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.