# Convolutional Neural Networks for Page Segmentation of Historical Document Images

arXiv: https://arxiv.org/abs/1704.01474 · HTML: https://mlsanity.com/paper/1704.01474/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2017.

## Built on by (most cited first, 12 of 12)

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- [SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation](https://mlsanity.com/paper/2305.04609/) (2023)
- [Restoring and Mining the Records of the Joseon Dynasty via Neural Language Modeling and Machine Translation](https://mlsanity.com/paper/2104.05964/) (2021)
- [Line Graphics Digitization: A Step Towards Full Automation](https://mlsanity.com/paper/2307.02065/) (2023)
- [Processing the structure of documents: Logical Layout Analysis of historical newspapers in French](https://mlsanity.com/paper/2202.08125/) (2022)
- [GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation](https://mlsanity.com/paper/2402.11401/) (2024)
- [Lessons learned developing and using a machine learning model to automatically transcribe 2.3 million handwritten occupation codes](https://mlsanity.com/paper/2106.03996/) (2021)
- [Entry Separation using a Mixed Visual and Textual Language Model: Application to 19th century French Trade Directories](https://mlsanity.com/paper/2302.08948/) (2023)
- [Understanding in Artificial Intelligence](https://mlsanity.com/paper/2101.06573/) (2021)
- [Unsupervised Deep Learning for Handwritten Page Segmentation](https://mlsanity.com/paper/2101.07487/) (2021)
- [Accurate Fine-grained Layout Analysis for the Historical Tibetan Document Based on the Instance Segmentation](https://mlsanity.com/paper/2110.08164/) (2021)
- [A Multistage Extraction Pipeline for Long Scanned Financial Documents: An Empirical Study in Industrial KYC Workflows](https://mlsanity.com/paper/2604.26462/) (2026)

## Abstract (arXiv)

This paper presents a Convolutional Neural Network (CNN) based page segmentation method for handwritten historical document images. We consider page segmentation as a pixel labeling problem, i.e., each pixel is classified as one of the predefined classes. Traditional methods in this area rely on carefully hand-crafted features or large amounts of prior knowledge. In contrast, we propose to learn features from raw image pixels using a CNN. While many researchers focus on developing deep CNN architectures to solve different problems, we train a simple CNN with only one convolution layer. We show that the simple architecture achieves competitive results against other deep architectures on different public datasets. Experiments also demonstrate the effectiveness and superiority of the proposed method compared to previous methods.