Zoom Text Detector

TL;DR

The paper introduces Zoom Text Detector (ZTD), a novel method that improves text detection accuracy by addressing shrink-mask reliability issues through zoom-inspired modules and a visual discriminator.

Contribution

It proposes ZTD with Zoom Out and Zoom In modules for better feature extraction and margin recognition, and a Sequential-Visual Discriminator to reduce false positives.

Findings

ZTD achieves superior detection accuracy and robustness.

The modules effectively address shrink-mask ambiguities.

Experimental results demonstrate improved comprehensive performance.

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.