PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition View Full Text


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Article Info

DATE

2022-08-20

AUTHORS

Dezhi Peng, Lianwen Jin, Yuliang Liu, Canjie Luo, Songxuan Lai

ABSTRACT

Handwritten Chinese text recognition (HCTR) has been an active research topic for decades. However, most previous studies solely focus on the recognition of cropped text line images, ignoring the error caused by text line detection in real-world applications. Although some approaches aimed at page-level text recognition have been proposed in recent years, they either are limited to simple layouts or require very detailed annotations including expensive line-level and even character-level bounding boxes. To this end, we propose PageNet for end-to-end weakly supervised page-level HCTR. PageNet detects and recognizes characters and predicts the reading order between them, which is more robust and flexible when dealing with complex layouts including multi-directional and curved text lines. Utilizing the proposed weakly supervised learning framework, PageNet requires only transcripts to be annotated for real data; however, it can still output detection and recognition results at both the character and line levels, avoiding the labor and cost of labeling bounding boxes of characters and text lines. Extensive experiments conducted on five datasets demonstrate the superiority of PageNet over existing weakly supervised and fully supervised page-level methods. These experimental results may spark further research beyond the realms of existing methods based on connectionist temporal classification or attention. The source code is available at https://github.com/shannanyinxiang/PageNet. More... »

PAGES

2623-2645

References to SciGraph publications

  • 2014-12-23. Label Embedding: A Frugal Baseline for Text Recognition in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2015-05-07. Reading Text in the Wild with Convolutional Neural Networks in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2018-10-06. Start, Follow, Read: End-to-End Full-Page Handwriting Recognition in COMPUTER VISION – ECCV 2018
  • 2018-10-07. Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes in COMPUTER VISION – ECCV 2018
  • 2021-01-05. Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2021-04-19. Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2020-10-24. Residual Dual Scale Scene Text Spotting by Fusing Bottom-Up and Top-Down Processing in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2018-06-15. A comprehensive study of hybrid neural network hidden Markov model for offline handwritten Chinese text recognition in INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION (IJDAR)
  • 2020-08-14. High Performance Offline Handwritten Chinese Text Recognition with a New Data Preprocessing and Augmentation Pipeline in DOCUMENT ANALYSIS SYSTEMS
  • 2018-10-09. Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes in COMPUTER VISION – ECCV 2018
  • 2020-02-10. Bottom-Up Scene Text Detection with Markov Clustering Networks in INTERNATIONAL JOURNAL OF COMPUTER VISION
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    URI

    http://scigraph.springernature.com/pub.10.1007/s11263-022-01654-0

    DOI

    http://dx.doi.org/10.1007/s11263-022-01654-0

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