PixelLink: Detecting Scene Text via Instance Segmentation

TL;DR

PixelLink introduces an instance segmentation approach for scene text detection that links pixels within the same text instance, enabling direct extraction of bounding boxes without regression, leading to improved efficiency and comparable accuracy.

Contribution

The paper presents PixelLink, a novel instance segmentation method for scene text detection that eliminates the need for bounding box regression, simplifying the detection process.

Findings

Achieves better or comparable performance on benchmarks

Requires fewer training iterations

Uses less training data

Abstract

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 many fewer training iterations and less training data.