# Joint Layout Analysis, Character Detection and Recognition for Historical Document Digitization

arXiv: https://arxiv.org/abs/2007.06890 · HTML: https://mlsanity.com/paper/2007.06890/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 21 of 21)

- [Page Layout Analysis System for Unconstrained Historic Documents](https://mlsanity.com/paper/2102.11838/) (2021)
- [MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression](https://mlsanity.com/paper/2307.07662/) (2023)
- [Predicting the Original Appearance of Damaged Historical Documents](https://mlsanity.com/paper/2412.11634/) (2024)
- [PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition](https://mlsanity.com/paper/2207.14807/) (2022)
- [DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning](https://mlsanity.com/paper/2504.04085/) (2025)
- [Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration](https://mlsanity.com/paper/2507.05108/) (2025)
- [C3Bench: A Comprehensive Classical Chinese Understanding Benchmark for Large Language Models](https://mlsanity.com/paper/2405.17732/) (2024)
- [MegaHan97K: A Large-Scale Dataset for Mega-Category Chinese Character Recognition with over 97K Categories](https://mlsanity.com/paper/2506.04807/) (2025)
- [MCCD: A Multi-Attribute Chinese Calligraphy Character Dataset Annotated with Script Styles, Dynasties, and Calligraphers](https://mlsanity.com/paper/2507.06948/) (2025)
- [Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts](https://mlsanity.com/paper/2108.09436/) (2021)
- [SegHist: A General Segmentation-based Framework for Chinese Historical Document Text Line Detection](https://mlsanity.com/paper/2406.15485/) (2024)
- [Cross-Domain Document Layout Analysis Using Document Style Guide](https://mlsanity.com/paper/2201.09407/) (2022)
- [VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts](https://mlsanity.com/paper/2304.00746/) (2023)
- [A Survey on Change Detection Techniques in Document Images](https://mlsanity.com/paper/2307.07691/) (2023)
- [RVAFM: Re-parameterizing Vertical Attention Fusion Module for Handwritten Paragraph Text Recognition](https://mlsanity.com/paper/2503.03104/) (2025)
- [CalliReader: Contextualizing Chinese Calligraphy via an Embedding-Aligned Vision-Language Model](https://mlsanity.com/paper/2503.06472/) (2025)
- [KuiSCIMA v2.0: Improved Baselines, Calibration, and Cross-Notation Generalization for Historical Chinese Music Notations in Jiang Kui's Baishidaoren Gequ](https://mlsanity.com/paper/2507.18741/) (2025)
- [FP-THD: Full page transcription of historical documents](https://mlsanity.com/paper/2601.17040/) (2026)
- [Text region detection in historical astronomical diagrams](https://mlsanity.com/paper/2606.15886/) (2026)
- [TongGuOCR: A Layout-Aware and Token-Augmented OCR MLLM for Chinese Historical Documents](https://mlsanity.com/paper/2608.07917/) (2026)
- [LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding](https://mlsanity.com/paper/2610.14531/) (2026)

## Abstract (arXiv)

In this paper, we propose an end-to-end trainable framework for restoring historical documents content that follows the correct reading order. In this framework, two branches named character branch and layout branch are added behind the feature extraction network. The character branch localizes individual characters in a document image and recognizes them simultaneously. Then we adopt a post-processing method to group them into text lines. The layout branch based on fully convolutional network outputs a binary mask. We then use Hough transform for line detection on the binary mask and combine character results with the layout information to restore document content. These two branches can be trained in parallel and are easy to train. Furthermore, we propose a re-score mechanism to minimize recognition error. Experiment results on the extended Chinese historical document MTHv2 dataset demonstrate the effectiveness of the proposed framework.