DoT-Net: Document Layout Classification Using Texture-Based CNN
| Autor: | Sai Chandra Kosaraju, Mohammed Masum, Nelson Zange Tsaku, Mingon Kang, Pritesh Patel, Tanju Bayramoglu, Girish Modgil |
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| Rok vydání: | 2019 |
| Předmět: |
Machine translation
Computer science business.industry 020207 software engineering Pattern recognition 02 engineering and technology Optical character recognition computer.software_genre Document layout Convolutional neural network 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence F1 score business Classifier (UML) computer Document layout analysis |
| Zdroj: | ICDAR |
| Popis: | 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 state-of-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. |
| Databáze: | OpenAIRE |
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