Zoom Text Detector

• URL: http://arxiv.org/abs/2209.03014v1
• Date: Wed, 7 Sep 2022 09:19:21 GMT
• Title: Zoom Text Detector
• Authors: Chuang. Yang, Mulin. Chen, Yuan. Yuan, and Qi. Wang
• Abstract summary: Text detectors adopt shrink-mask based representation strategies. Unfortunately, three disadvantages cause unreliable shrink-masks. We propose a Zoom Text Detector inspired by the zoom process of the camera.
• Score: 26.761735112547953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To pursue comprehensive performance, recent text detectors improve detection speed at the expense of accuracy. They adopt shrink-mask based text representation strategies, which leads to a high dependency of detection accuracy on shrink-masks. Unfortunately, three disadvantages cause unreliable shrink-masks. Specifically, these methods try to strengthen the discrimination of shrink-masks from the background by semantic information. However, the feature defocusing phenomenon that coarse layers are optimized by fine-grained objectives limits the extraction of semantic features. Meanwhile, since both shrink-masks and the margins belong to texts, the detail loss phenomenon that the margins are ignored hinders the distinguishment of shrink-masks from the margins, which causes ambiguous shrink-mask edges. Moreover, false-positive samples enjoy similar visual features with shrink-masks. They aggravate the decline of shrink-masks recognition. To avoid the above problems, we propose a Zoom Text Detector (ZTD) inspired by the zoom process of the camera. Specifically, Zoom Out Module (ZOM) is introduced to provide coarse-grained optimization objectives for coarse layers to avoid feature defocusing. Meanwhile, Zoom In Module (ZIM) is presented to enhance the margins recognition to prevent detail loss. Furthermore, Sequential-Visual Discriminator (SVD) is designed to suppress false-positive samples by sequential and visual features. Experiments verify the superior comprehensive performance of ZTD.

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