Pyramid Mask Text Detector

Abstract

Scene text detection, an essential step of scene text recognition system, is to locate text instances in natural scene images automatically. Some recent attempts benefiting from Mask R-CNN formulate scene text detection task as an instance segmentation problem and achieve remarkable performance. In this paper, we present a new Mask R-CNN based framework named Pyramid Mask Text Detector (PMTD) to handle the scene text detection. Instead of binary text mask generated by the existing Mask R-CNN based methods, our PMTD performs pixel-level regression under the guidance of location-aware supervision, yielding a more informative soft text mask for each text instance. As for the generation of text boxes, PMTD reinterprets the obtained 2D soft mask into 3D space and introduces a novel plane clustering algorithm to derive the optimal text box on the basis of 3D shape. Experiments on standard datasets demonstrate that the proposed PMTD brings consistent and noticeable gain and clearly outperforms state-of-the-art methods. Specifically, it achieves an F-measure of 80.13% on ICDAR 2017 MLT dataset.

References in corpus (11)

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Cited by in corpus (11)

• Text Detection and Recognition in the Wild: A Review
• MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding
• ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT
• LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment
• Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
• RRPN++: Guidance Towards More Accurate Scene Text Detection
• Character Region Attention For Text Spotting
• Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection
• AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting
• CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks
• OSKDet: Towards Orientation-sensitive Keypoint Localization for Rotated Object Detection