A selectional auto-encoder approach for document image binarization

doi:10.1016/j.patcog.2018.08.011

Return

A selectional auto-encoder approach for document image binarization

2019-02-01

103

Jorge Calvo-Zaragoza *

Antonio‐Javier Gallego

DOI：10.1016/j.patcog.2018.08.011

Original

Save

View PDF

Abstract

En 中文

Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of document analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a global threshold. This approach has proven to outperform existing binarization strategies in a number of document types. (C) 2018 Elsevier Ltd. All rights reserved.

Keywords:

Binarization

Document analysis

Auto-encoders

Convolutional Neural Networks

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.