Text in curve orientation, despite being one of the common text orientations in real world environment, has close to zero existence in well received scene text datasets such as ICDAR2013 and MSRA-TD500. The main motivation of Total-Text is to fill this gap and facilitate a new research direction for the scene text community. On top of the conventional horizontal and multi-oriented texts, it features curved-oriented text. Total-Text is highly diversified in orientations, more than half of its images have a combination of more than two orientations. Recently, a new breed of solutions that casted text detection as a segmentation problem has demonstrated their effectiveness against multi-oriented text. In order to evaluate its robustness against curved text, we fine-tuned DeconvNet and benchmark it on Total-Text. Total-Text with its annotation is available at https://github.com/cs-chan/Total-Text-Dataset
@article{arxiv.1710.10400,
title = {Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition},
author = {Chee Kheng Chng and Chee Seng Chan},
journal= {arXiv preprint arXiv:1710.10400},
year = {2017}
}
Comments
Accepted as Oral presentation in ICDAR2017 (Extended version, 13 pages 17 figures). We introduce a new scene text dataset namely as Total-Text, which is more comprehensive than the existing scene text datasets as it consists of 1555 natural images with more than 3 different text orientations, one of a kind