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Register a new userRayNet: Real-time Scene Arbitrary-shape Text Detection with Multiple Rays
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Added by
Chuang Yang
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Existing object detection-based text detectors mainly concentrate on detecting horizontal and multioriented text. However, they do not pay enough attention to complex-shape text (curved or other irregularly shaped text). Recently, segmentation-based text detection methods have been introduced to deal with the complex-shape text; however, the pixel level processing increases the computational cost significantly. To further improve the accuracy and efficiency, we propose a novel detection framework for arbitrary-shape text detection, termed as RayNet. RayNet uses Center Point Set (CPS) and Ray Distance (RD) to fit text, where CPS is used to determine the text general position and the RD is combined with CPS to compute Ray Points (RP) to localize the text accurate shape. Since RP are disordered, we develop the Ray Points Connection (RPC) algorithm to reorder RP, which significantly improves the detection performance of complex-shape text. RayNet achieves impressive performance on existing curved text dataset (CTW1500) and quadrangle text dataset (ICDAR2015), which demonstrate its superiority against several state-of-the-art methods.
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Numerous scene text detection methods have been proposed in recent years. Most of them declare they have achieved state-of-the-art performances. However, the performance comparison is unfair, due to lots of inconsistent settings (e.g., training data, backbone network, multi-scale feature fusion, evaluation protocols, etc.). These various settings would dissemble the pros and cons of the proposed core techniques. In this paper, we carefully examine and analyze the inconsistent settings, and propose a unified framework for the bottom-up based scene text detection methods. Under the unified framework, we ensure the consistent settings for non-core modules, and mainly investigate the representations of describing arbitrary-shape scene texts, e.g., regressing points on text contours, clustering pixels with predicted auxiliary information, grouping connected components with learned linkages, etc. With the comprehensive investigations and elaborate analyses, it not only cleans up the obstacle of understanding the performance differences between existing methods but also reveals the advantages and disadvantages of previous models under fair comparisons.
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. However, the post-processing of binarization is essential for segmentation-based detection, which converts probability maps produced by a segmentation method into bounding boxes/regions of text. In this paper, we propose a module named Differentiable Binarization (DB), which can perform the binarization process in a segmentation network. Optimized along with a DB module, a segmentation network can adaptively set the thresholds for binarization, which not only simplifies the post-processing but also enhances the performance of text detection. Based on a simple segmentation network, we validate the performance improvements of DB on five benchmark datasets, which consistently achieves state-of-the-art results, in terms of both detection accuracy and speed. In particular, with a light-weight backbone, the performance improvements by DB are significant so that we can look for an ideal tradeoff between detection accuracy and efficiency. Specifically, with a backbone of ResNet-18, our detector achieves an F-measure of 82.8, running at 62 FPS, on the MSRA-TD500 dataset. Code is available at: https://github.com/MhLiao/DB
Segmentation-based scene text detection methods have been widely adopted for arbitrary-shaped text detection recently, since they make accurate pixel-level predictions on curved text instances and can facilitate real-time inference without time-consuming processing on anchors. However, current segmentation-based models are unable to learn the shapes of curved texts and often require complex label assignments or repeated feature aggregations for more accurate detection. In this paper, we propose RSCA: a Real-time Segmentation-based Context-Aware model for arbitrary-shaped scene text detection, which sets a strong baseline for scene text detection with two simple yet effective strategies: Local Context-Aware Upsampling and Dynamic Text-Spine Labeling, which model local spatial transformation and simplify label assignments separately. Based on these strategies, RSCA achieves state-of-the-art performance in both speed and accuracy, without complex label assignments or repeated feature aggregations. We conduct extensive experiments on multiple benchmarks to validate the effectiveness of our method. RSCA-640 reaches 83.9% F-measure at 48.3 FPS on CTW1500 dataset.
The latest trend in the bottom-up perspective for arbitrary-shape scene text detection is to reason the links between text segments using Graph Convolutional Network (GCN). Notwithstanding, the performance of the best performing bottom-up method is still inferior to that of the best performing top-down method even with the help of GCN. We argue that this is not mainly caused by the limited feature capturing ability of the text proposal backbone or GCN, but by their failure to make a full use of visual-relational features for suppressing false detection, as well as the sub-optimal route-finding mechanism used for grouping text segments. In this paper, we revitalize the classic text detection frameworks by aggregating the visual-relational features of text with two effective false positive/negative suppression mechanisms. First, dense overlapping text segments depicting the `characterness and `streamline of text are generated for further relational reasoning and weakly supervised segment classification. Here, relational graph features are used for suppressing false positives/negatives. Then, to fuse the relational features with visual features, a Location-Aware Transfer (LAT) module is designed to transfer texts relational features into visual compatible features with a Fuse Decoding (FD) module to enhance the representation of text regions for the second step suppression. Finally, a novel multiple-text-map-aware contour-approximation strategy is developed, instead of the widely-used route-finding process. Experiments conducted on five benchmark datasets, i.e., CTW1500, Total-Text, ICDAR2015, MSRA-TD500, and MLT2017 demonstrate that our method outperforms the state-of-the-art performance when being embedded in a classic text detection framework, which revitalises the superb strength of the bottom-up methods.
Arbitrary shape text detection is a challenging task due to the high complexity and variety of scene texts. In this work, we propose a novel adaptive boundary proposal network for arbitrary shape text detection, which can learn to directly produce accurate boundary for arbitrary shape text without any post-processing. Our method mainly consists of a boundary proposal model and an innovative adaptive boundary deformation model. The boundary proposal model constructed by multi-layer dilated convolutions is adopted to produce prior information (including classification map, distance field, and direction field) and coarse boundary proposals. The adaptive boundary deformation model is an encoder-decoder network, in which the encoder mainly consists of a Graph Convolutional Network (GCN) and a Recurrent Neural Network (RNN). It aims to perform boundary deformation in an iterative way for obtaining text instance shape guided by prior information from the boundary proposal model. In this way, our method can directly and efficiently generate accurate text boundaries without complex post-processing. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method.
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