Towards End-to-End Text Spotting with Convolutional Recurrent Neural Networks
Open accessAbstract
In this work, we jointly address the problem of text detection and recognition in natural scene images based on convolutional recurrent neural networks. We propose a unified network that simultaneously localizes and recognizes text with a single forward pass, avoiding intermediate processes, such as image cropping, feature re-calculation, word separation, and character grouping. In contrast to existing approaches that consider text detection and recognition as two distinct tasks and tackle them one by one, the proposed framework settles these two tasks concurrently. The whole framework can be trained end-to-end, requiring only images, ground-truth bounding boxes and text labels. The convolutional features are calculated only once and shared by both detection and recognition, which saves processing time. Through multi-task training, the learned features become more informative and improves the overall performance. Our proposed method has achieved competitive performance on several benchmark datasets.
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End-to-end modeling of text detection and recognition has achieved impressive performance.
“Recently, end-toend modeling of text detection and recognition has achieved impressive performance [90], [91].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [90]
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“Curriculum Learning In order to improve generalization and accelerate the convergence speed, we design a curriculum learning [1] paradigm to train the model from gradually more complex data.”
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FOTS: Fast Oriented Text Spotting with a Unified Network
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PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text
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Self-citation
- Authors
- Li, H., Wang, P., & Shen, C.
- Affiliations
- Australian Centre for Robotic Vision (AU)
- Northwestern Polytechnical University (CN)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 5248-5256
- OpenAlex
- W2964296749
- Cited by
- 230
- References
- 7
- Citations by year
- 2026: 6 · 2025: 20 · 2024: 24 · 2023: 23 · 2022: 37 · 2021: 38 · 2020: 38 · 2019: 27 · 2018: 16 · 2017: 1
- FWCI
- 6.55
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Physical Sciences › Engineering › Media Technology › Vehicle License Plate Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- text spotting, text detection, text recognition, convolutional recurrent neural network, end-to-end learning, multi-task learning, natural images, text localization
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