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Scene Text Detection and Recognition: The Deep Learning Era

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arxiv 1811.04256 v5 pith:RJ74ZCHD submitted 2018-11-10 cs.CV

Shangbang Long , Xin He , Cong Yao This is my paper · ORCID
classification cs.CV
keywords deeplearningdetectionrecognitionscenetextbeencomputer
verification ladder T0 review T1 audit T2 compute T3 formal

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With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inescapably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, approach and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and the grand challenges still remained. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected and compiled in our Github repository: https://github.com/Jyouhou/SceneTextPapers.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards Unconstrained End-to-End Text Spotting

    cs.CV 2019-08 conditional novelty 7.0 of 10

    A Mask R-CNN and attention-based text spotter handles curved text by masking RoI features instead of rectifying them, and uses OCR-engine labels as extra training data to set state-of-the-art results on ICDAR15 and To...

  2. Editing Text in the Wild

    cs.CV 2019-08 conditional novelty 7.0 of 10

    SRNet edits words in natural images end-to-end by separating text style transfer from background inpainting and fusing them into a realistic result.

  3. Rethinking Irregular Scene Text Recognition

    cs.CV 2019-08 conditional novelty 6.0 of 10

    Curved synthetic training data, aspect-ratio-preserving resizing, and rotation augmentation push rectification-based scene text recognizers to state-of-the-art accuracy on curved text benchmarks.

  4. Adaptive Embedding Gate for Attention-Based Scene Text Recognition

    cs.CV 2019-08 conditional novelty 6.0 of 10

    A gating module that modulates the previous-prediction embedding by estimated character-pair correlations improves attention-based scene text recognition accuracy and noise robustness.

  5. Symmetry-constrained Rectification Network for Scene Text Recognition

    cs.CV 2019-08 conditional novelty 6.0 of 10

    A symmetry-constrained rectification network that predicts per-character geometry improves scene text recognition on regular and irregular text, achieving state-of-the-art on CUTE80, SVTP, and ICDAR 2015.

  6. Curved Text Detection in Natural Scene Images with Semi- and Weakly-Supervised Learning

    cs.CV 2019-08 conditional novelty 5.0 of 10

    A semi- and weakly-supervised scheme trains curved text detectors with 10% pixel-level labels plus weak rectangle labels, reaching performance near fully supervised state-of-the-art on CTW1500 and Total-Text.

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