Title

DoT-Net: Document Layout Classification Using Texture-based CNN

Document Type

Conference Proceeding

Publication Date

9-20-2019

Publication Title

The 15th International Conference on Document Analysis and Recognition (ICDAR)

Publisher

Springer, Cham

First page number:

1029

Last page number:

1034

Abstract

Document Layout Analysis (DLA) is a segmentation process that decomposes a scanned document image into its blocks of interest and classifies them. DLA is essential in a large number of applications, such as Information Retrieval, Machine Translation, Optical Character Recognition (OCR) systems, and structured data extraction from documents. However, identification of document blocks in DLA is challenging due to variations of block locations, inter- and intra- class variability, and background noises. In this paper, we propose a novel texture-based convolutional neural network for document layout analysis, called DoT-Net. DoT-Net is a multiclass classifier that can effectively identify document component blocks such as text, image, table, mathematical expression, and line-diagram, whereas most related methods have focused on the text vs. non-text block classification problem. DoT-Net can capture textural variations among the multiclass regions of documents. Our proposed method DoT-Net achieved promising results outperforming stateof-the-art document layout classifiers on accuracy, F1 score, and AUC. The open-source code of DoT-Net is available at https://github.com/datax-lab/DoTNet.

Keywords

Document Layout Analysis; Dilated CNN; Texture Based Document Analysis

Disciplines

Computer Sciences | Databases and Information Systems | Physical Sciences and Mathematics

Language

English

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