Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes

Abstract

Recently, models based on deep neural networks have dominated the fields of scene text detection and recognition. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images. An end-to-end trainable neural network model for scene text spotting is proposed. The proposed model, named as Mask TextSpotter, is inspired by the newly published work Mask R-CNN. Different from previous methods that also accomplish text spotting with end-to-end trainable deep neural networks, Mask TextSpotter takes advantage of simple and smooth end-to-end learning procedure, in which precise text detection and recognition are acquired via semantic segmentation. Moreover, it is superior to previous methods in handling text instances of irregular shapes, for example, curved text. Experiments on ICDAR2013, ICDAR2015 and Total-Text demonstrate that the proposed method achieves state-of-the-art results in both scene text detection and end-to-end text recognition tasks.

arXiv commentTo appear in ECCV 2018

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Cited by in corpus (14)

• Character Region Awareness for Text Detection
• Scene Text Recognition from Two-Dimensional Perspective
• Text Detection and Recognition in the Wild: A Review
• Scene Text Detection with Supervised Pyramid Context Network
• TextMountain: Accurate Scene Text Detection via Instance Segmentation
• Geometry Normalization Networks for Accurate Scene Text Detection
• Convolutional Character Networks
• SynthText3D: Synthesizing Scene Text Images from 3D Virtual Worlds
• Primitive Representation Learning for Scene Text Recognition
• Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting
• Mask-aware networks for crowd counting
• Adaptive Text Recognition through Visual Matching
• Learning to Predict More Accurate Text Instances for Scene Text Detection
• CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks