Scene Text Detection is a task to detect text regions in the complex background and label them with bounding boxes.
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text.
Ranked #2 on Scene Text Detection on MSRA-TD500
We propose a novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image.
The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images.
Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts.
Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances.
Ranked #5 on Scene Text Detection on SCUT-CTW1500
To address these problems, we propose a novel Progressive Scale Expansion Network (PSENet), designed as a segmentation-based detector with multiple predictions for each text instance.
Ranked #6 on Scene Text Detection on SCUT-CTW1500
In this paper, we present an end-to-end trainable fast scene text detector, named TextBoxes++, which detects arbitrary-oriented scene text with both high accuracy and efficiency in a single network forward pass.
Ranked #2 on Scene Text Detection on COCO-Text