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

arXiv: https://arxiv.org/abs/2207.14807 · HTML: https://mlsanity.com/paper/2207.14807/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 66 references that are in the map.
- First posted on arXiv 29 Jul 2022.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition](https://mlsanity.com/paper/1406.2227/) (2014)
- [Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes](https://mlsanity.com/paper/1908.08207/) (2019)
- [ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard](https://mlsanity.com/paper/1912.09641/) (2019)
- [Reading Text in the Wild with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1842/) (2014)
- [FOTS: Fast Oriented Text Spotting with a Unified Network](https://mlsanity.com/paper/1801.01671/) (2018)
- [Convolutional Character Networks](https://mlsanity.com/paper/1910.07954/) (2019)
- [OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold](https://mlsanity.com/paper/2006.07491/) (2020)
- [Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition](https://mlsanity.com/paper/1604.08352/) (2016)
- [Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention](https://mlsanity.com/paper/1604.03286/) (2016)
- [Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes](https://mlsanity.com/paper/1807.03021/) (2018)
- [ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild](https://mlsanity.com/paper/1808.09568/) (2018)
- [Drawing and Recognizing Chinese Characters with Recurrent Neural Network](https://mlsanity.com/paper/1606.06539/) (2016)
- [Aggregation Cross-Entropy for Sequence Recognition](https://mlsanity.com/paper/1904.08364/) (2019)
- [Joint Layout Analysis, Character Detection and Recognition for Historical Document Digitization](https://mlsanity.com/paper/2007.06890/) (2020)
- [Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition](https://mlsanity.com/paper/1610.02616/) (2016)
- [Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild](https://mlsanity.com/paper/2001.04189/) (2020)
- [Writer-Aware CNN for Parsimonious HMM-Based Offline Handwritten Chinese Text Recognition](https://mlsanity.com/paper/1812.09809/) (2018)
- [A Computationally Efficient Pipeline Approach to Full Page Offline Handwritten Text Recognition](https://mlsanity.com/paper/1910.00663/) (2019)

## Built on by (most cited first, 8 of 8)

- [Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation](https://mlsanity.com/paper/2310.16809/) (2023)
- [UPOCR: Towards Unified Pixel-Level OCR Interface](https://mlsanity.com/paper/2312.02694/) (2023)
- [End-to-End Page-Level Assessment of Handwritten Text Recognition](https://mlsanity.com/paper/2301.05935/) (2023)
- [SwinTextSpotter v2: Towards Better Synergy for Scene Text Spotting](https://mlsanity.com/paper/2401.07641/) (2024)
- [VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts](https://mlsanity.com/paper/2304.00746/) (2023)
- [RVAFM: Re-parameterizing Vertical Attention Fusion Module for Handwritten Paragraph Text Recognition](https://mlsanity.com/paper/2503.03104/) (2025)
- [Improving Handwritten Text Recognition via 3D Attention and Multi-Scale Training](https://mlsanity.com/paper/2410.18374/) (2024)
- [CalliReader: Contextualizing Chinese Calligraphy via an Embedding-Aligned Vision-Language Model](https://mlsanity.com/paper/2503.06472/) (2025)

## Abstract (arXiv)

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.