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ABSTRACT Thanks to the development of information extraction models, it is possible to digitize and extract important information in document images quickly and efficiently.. In this the



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ABSTRACT

Thanks to the development of information extraction models, it is possible to digitize and extract important information in document images quickly and efficiently In this thesis, we present an information extraction model using deep learning on graphs This is one of the newest and promising techniques for information extraction problems However, to complete a problem of extracting information in document images, we need to do the following steps: Object detection, Text detection and Optical character recognition These are also things that we have researched and tested

Regarding the proposed method, Object detection will be approached by Mask R-CNN model, Text detection will use text area detection method with CTPN model, Optical character recognition will adopt Tesseract OCR, which is developed by Google, and Information extraction will be approached by classifying text regions using a graph convolutional neural network model (GCN and GraphSAGE) For each process, we tested and evaluated our method with the collected dataset

We tested the system on a dataset of English business cards of several companies Because the business card is a highly random, diverse, and useful form of data in practice Besides, we also conduct system evaluation in comparison to some commercial products on the market such as Abbyy, BizConnect

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OCR Optical Character Recognition

GNN Graph Neural Networks

RPN Region Proposal Network

ROI Region of Interest

CNN Convolutional Neural Network

RNN Recurrent Neural Networks

ANN Artificial Neural Network

NLP Natural Language Processing

GCNs Graph Convolutional Networks

CTPN Connectionist Text Proposal Network FAIR Facebook AI Research

CER Character Error Rate

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