Robust ReadingCompetition
Challenges

Authors: Wenhai Wang, Xiang Li, Wenbo Hou, Tong Lu, Jian Yang

Description: A text detector based on semantic segmentation. Using only ICDAR_2017 MLT training set and ICDAR 2015 training set. Paper is in the preparation. And we will release our code latter.

method: PixelLink2017-09-13

Authors: Dan Deng

Description: PixelLink: Detecting Scene Text via Instance Segmentation

Accepted by AAAI2018

ABSTRCT:
Most state-of-the-art scene text detection algorithms are deep learning based methods that depend on bounding box regression and perform at least two kinds of predictions: text/non-text classification and location regression. Regression plays a key role in the acquisition of bounding boxes in these methods, but it is not indispensable, because text/non-text prediction can also be considered as a kind of semantic segmentation that contains full location information in itself. However, text instances in scene images often lie very close to each other, making them very difficult to separate via semantic segmentation. Therefore, instance segmentation is needed to address this problem. In this paper, PixelLink, a novel scene text detection algorithm based on instance segmentation, is proposed. Text instances are first segmented out by linking pixels within the same instance together. Text bounding boxes are then extracted directly from the segmentation result without location regression. Experiments show that, compared with regression based methods, PixelLink can achieve better or comparable performance on several benchmarks, while requiring much fewer training iterations and less training data.

Using only the 1,000 images in IC15-train, the best performance is 83.7%; when SynthText is added for pretraining, it is 85%

method: crpn2018-01-04

Authors: Linjie Deng, Yanxiang Gong, Yi Lin

Description: This is a two-stage detection framework for multi-oriented scene text. It employs corners to estimate the possible locations of text instances and a region-wise subnetwork for further classification and regression. Paper and code are publicly available now.

Ranking Table

Description Paper Source Code
DateMethodRecallPrecisionHmean
2018-05-18PSENet_NJU_ImagineLab (single-scale)85.22%89.30%87.21%
2017-09-13PixelLink83.77%86.65%85.19%
2018-01-04crpn80.69%88.77%84.54%
2017-07-31EAST reimplemention with resnet 5077.32%84.66%80.83%
2017-01-23RRPN-477.13%83.52%80.20%
2016-10-28RRPN-373.23%82.17%77.44%

Ranking Graphic