# PCA-Initialized Deep Neural Networks Applied To Document Image Analysis

## TL;DR

This paper introduces a PCA-based initialization method for deep neural networks, transforming PCA into auto-encoders, which improves stability and performance in document layout analysis compared to traditional random initialization.

## Contribution

The paper presents a novel PCA-based initialization technique for neural networks, turning PCA into auto-encoders, and demonstrates its effectiveness in document image analysis tasks.

## Key findings

- PCA-based initialization is quick and stable.
- It outperforms random weight initialization in layout analysis.
- The method enhances training efficiency and accuracy.

## Abstract

In this paper, we present a novel approach for initializing deep neural networks, i.e., by turning PCA into neural layers. Usually, the initialization of the weights of a deep neural network is done in one of the three following ways: 1) with random values, 2) layer-wise, usually as Deep Belief Network or as auto-encoder, and 3) re-use of layers from another network (transfer learning). Therefore, typically, many training epochs are needed before meaningful weights are learned, or a rather similar dataset is required for seeding a fine-tuning of transfer learning. In this paper, we describe how to turn a PCA into an auto-encoder, by generating an encoder layer of the PCA parameters and furthermore adding a decoding layer. We analyze the initialization technique on real documents. First, we show that a PCA-based initialization is quick and leads to a very stable initialization. Furthermore, for the task of layout analysis we investigate the effectiveness of PCA-based initialization and show that it outperforms state-of-the-art random weight initialization methods.