A semi- and weakly-supervised scheme trains curved text detectors with 10% pixel-level labels plus weak rectangle labels, reaching performance near fully supervised state-of-the-art on CTW1500 and Total-Text.
Detecting Oriented Text in Natural Images by Linking Segments
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abstract
Most state-of-the-art text detection methods are specific to horizontal Latin text and are not fast enough for real-time applications. We introduce Segment Linking (SegLink), an oriented text detection method. The main idea is to decompose text into two locally detectable elements, namely segments and links. A segment is an oriented box covering a part of a word or text line; A link connects two adjacent segments, indicating that they belong to the same word or text line. Both elements are detected densely at multiple scales by an end-to-end trained, fully-convolutional neural network. Final detections are produced by combining segments connected by links. Compared with previous methods, SegLink improves along the dimensions of accuracy, speed, and ease of training. It achieves an f-measure of 75.0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin. It runs at over 20 FPS on 512x512 images. Moreover, without modification, SegLink is able to detect long lines of non-Latin text, such as Chinese.
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Curved Text Detection in Natural Scene Images with Semi- and Weakly-Supervised Learning
A semi- and weakly-supervised scheme trains curved text detectors with 10% pixel-level labels plus weak rectangle labels, reaching performance near fully supervised state-of-the-art on CTW1500 and Total-Text.