# Convolutional Neural Networks for Page Segmentation of Historical Document Images

## TL;DR

This paper introduces a simple CNN-based approach for segmenting handwritten historical document images, treating page segmentation as pixel classification, and demonstrates its competitive performance against more complex models.

## Contribution

Proposes a straightforward CNN architecture for page segmentation that learns features directly from raw pixels, outperforming traditional handcrafted feature methods.

## Key findings

- Simple CNN achieves competitive results on public datasets.
- Method outperforms previous traditional approaches.
- Effective pixel labeling for historical document images.

## Abstract

This paper presents a Convolutional Neural Network (CNN) based page segmentation method for handwritten historical document images.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.