Detecting Text in Natural Image with Connectionist Text Proposal Network

TL;DR

The paper introduces a Connectionist Text Proposal Network (CTPN) that accurately detects text lines in natural images using a novel, end-to-end trainable deep learning model that integrates convolutional features with sequential proposals.

Contribution

It presents the CTPN model which combines a vertical anchor mechanism with a recurrent neural network for precise, multi-scale, multi-language text detection without complex post-processing.

Findings

Achieves high F-measure on ICDAR benchmarks

Operates efficiently at 0.14 seconds per image

Outperforms recent state-of-the-art methods

Abstract

We propose a novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image. The CTPN detects a text line in a sequence of fine-scale text proposals directly in convolutional feature maps. We develop a vertical anchor mechanism that jointly predicts location and text/non-text score of each fixed-width proposal, considerably improving localization accuracy. The sequential proposals are naturally connected by a recurrent neural network, which is seamlessly incorporated into the convolutional network, resulting in an end-to-end trainable model. This allows the CTPN to explore rich context information of image, making it powerful to detect extremely ambiguous text. The CTPN works reliably on multi-scale and multi- language text without further post-processing, departing from previous bottom-up methods requiring multi-step post-processing. It achieves 0.88 and 0.61 F-measure on the ICDAR 2013 and 2015 benchmarks, surpass- ing recent results [8, 35] by a large margin. The CTPN is computationally efficient with 0:14s/image, by using the very deep VGG16 model [27]. Online demo is available at: http://textdet.com/.