Papers › Sliding Line Point Regression for Shape Robust Scene Text Detection

Sliding Line Point Regression for Shape Robust Scene Text Detection

30 Jan 2018arXiv:1801.09969archive 2025-07-28

Yixing Zhu, Jun Du

Traditional text detection methods mostly focus on quadrangle text. In this study we propose a novel method named sliding line point regression (SLPR) in order to detect arbitrary-shape text in natural scene. SLPR regresses multiple points on the edge of text line and then utilizes these points to sketch the outlines of the text. The proposed SLPR can be adapted to many object detection architectures such as Faster R-CNN and R-FCN. Specifically, we first generate the smallest rectangular box including the text with region proposal network (RPN), then isometrically regress the points on the edge of text by using the vertically and horizontally sliding lines. To make full use of information and reduce redundancy, we calculate x-coordinate or y-coordinate of target point by the rectangular box position, and just regress the remaining y-coordinate or x-coordinate. Accordingly we can not only reduce the parameters of system, but also restrain the points which will generate more regular polygon. Our approach achieved competitive results on traditional ICDAR2015 Incidental Scene Text benchmark and curve text detection dataset CTW1500.

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Code

Shualite/UBNet pytorch report

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Tasks

Curved Text Detection Object Detection Region Proposal Scene Text Detection Text Detection object-detection regression

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Scene Text Detection	SCUT-CTW1500	SLPR	F-Measure	74.8	#17 of 17	Archive leaderboard	report
Scene Text Detection	SCUT-CTW1500	SLPR	Precision	80.1	#17 of 17	Archive leaderboard	report
Scene Text Detection	SCUT-CTW1500	SLPR	Recall	70.1	#17 of 17	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Convolution Faster R-CNN Position-Sensitive RoI Pooling R-FCN RPN RoIPool Softmax

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