Adaptive Shrink-Mask for Text Detection

• URL: http://arxiv.org/abs/2111.09560v1
• Date: Thu, 18 Nov 2021 07:38:57 GMT
• Title: Adaptive Shrink-Mask for Text Detection
• Authors: Chuang Yang, Mulin Chen, Yuan Yuan, Qi Wang, Xuelong Li
• Abstract summary: Existing real-time text detectors reconstruct text contours by shrink-masks directly. The dependence on predicted shrink-masks leads to unstable detection results. Super-pixel Window (SPW) is designed to supervise the network.
• Score: 91.34459257409104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing real-time text detectors reconstruct text contours by shrink-masks directly, which simplifies the framework and can make the model run fast. However, the strong dependence on predicted shrink-masks leads to unstable detection results. Moreover, the discrimination of shrink-masks is a pixelwise prediction task. Supervising the network by shrink-masks only will lose much semantic context, which leads to the false detection of shrink-masks. To address these problems, we construct an efficient text detection network, Adaptive Shrink-Mask for Text Detection (ASMTD), which improves the accuracy during training and reduces the complexity of the inference process. At first, the Adaptive Shrink-Mask (ASM) is proposed to represent texts by shrink-masks and independent adaptive offsets. It weakens the coupling of texts to shrink-masks, which improves the robustness of detection results. Then, the Super-pixel Window (SPW) is designed to supervise the network. It utilizes the surroundings of each pixel to improve the reliability of predicted shrink-masks and does not appear during testing. In the end, a lightweight feature merging branch is constructed to reduce the computational cost. As demonstrated in the experiments, our method is superior to existing state-of-the-art (SOTA) methods in both detection accuracy and speed on multiple benchmarks.

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