ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)

Abstract

Chinese is the most widely used language in the world. Algorithms that read Chinese text in natural images facilitate applications of various kinds. Despite the large potential value, datasets and competitions in the past primarily focus on English, which bares very different characteristics than Chinese. This report introduces RCTW, a new competition that focuses on Chinese text reading. The competition features a large-scale dataset with 12,263 annotated images. Two tasks, namely text localization and end-to-end recognition, are set up. The competition took place from January 20 to May 31, 2017. 23 valid submissions were received from 19 teams. This report includes dataset description, task definitions, evaluation protocols, and results summaries and analysis. Through this competition, we call for more future research on the Chinese text reading problem. The official website for the competition is http://rctw.vlrlab.net

Cited by in corpus (7)

• Scene Text Detection and Recognition: The Deep Learning Era
• Scene Text Synthesis for Efficient and Effective Deep Network Training
• TextMountain: Accurate Scene Text Detection via Instance Segmentation
• Rotation-Sensitive Regression for Oriented Scene Text Detection
• Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes
• Primitive Representation Learning for Scene Text Recognition
• Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping