TextBoxes: A Fast Text Detector with a Single Deep Neural Network

Abstract

This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression. TextBoxes outperforms competing methods in terms of text localization accuracy and is much faster, taking only 0.09s per image in a fast implementation. Furthermore, combined with a text recognizer, TextBoxes significantly outperforms state-of-the-art approaches on word spotting and end-to-end text recognition tasks.

Accepted by AAAI2017

References in corpus (1)

• Very Deep Convolutional Networks for Large-Scale Image Recognition

Cited by in corpus (10)

• R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
• Single Shot Text Detector with Regional Attention
• PixelLink: Detecting Scene Text via Instance Segmentation
• Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks
• Sliding Line Point Regression for Shape Robust Scene Text Detection
• Feature Enhancement Network: A Refined Scene Text Detector
• Deep Scene Text Detection with Connected Component Proposals
• GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition
• Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification
• Semi-Bagging Based Deep Neural Architecture to Extract Text from High Entropy Images