Research and Implementation of Data Extraction Method Based on NLP

Si, Yaqing; Zhou, Wendi; Gai, Jiale

doi:10.1109/asid50160.2020.9271745

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2023

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Synchronization of the Internship Registration System using a Study Plan and Acceptance Letter Based on Tesseract-OCR and Template Matching

Kirana,

Ningrum,

Soraya

et al. 2023

2023 8th International Conference on Electrical, Electronics and Information Engineering (ICEEIE)

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Synchronization of the Internship Registration System using a Study Plan and Acceptance Letter Based on Tesseract-OCR and Template Matching

Kirana,

Ningrum,

Soraya

et al. 2023

2023 8th International Conference on Electrical, Electronics and Information Engineering (ICEEIE)

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Development of an NLP-Based Automatic Data Retrieval Model

Kumar,

Kumar

2023

2023 4th International Conference on Smart Electronics and Communication (ICOSEC)

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Information extraction from Visually Rich Documents using graph convolutional network

Nguyen-Trong¹,

Trinh²

2023

IFS

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Visually rich documents, such as forms, invoices, receipts, and ID cards, are ubiquitous in daily business and life. Various methods have been used to convey such diverse information, including text, layout, font size, or text position. Combining these elements in information extraction can improve the result performance. However, previous works have not effectively utilized the cooperation between these rich information sources. Text detection and recognition have been performed without semantic supervision (e.g., entity name annotation), and text information extraction has been performed using only serialized plain text, ignoring rich visual information. This paper presents a method for extracting information from such documents, which integrates textual, non-spatial, and spatial visual features. The method consists of two main steps and uses three deep neural networks. The first step, Text Reading, employs two CNN models (Lightweight DB and C-PREN) for OCR tasks, based on the state-of-the-art models DB and PREN, with two improvements. These improvements include reducing noise by removing the SE block of DB and integrating both context and position features in PREN. The second step, Text Information Extraction, uses a graph convolutional network, RGCN, for name entity recognition. Experiments on self-collected and two public datasets have demonstrated that our method improves the performance of the original models and outperforms other state-of-the-art methods.

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Research and Implementation of Data Extraction Method Based on NLP

Cited by 3 publications

References 1 publication

Synchronization of the Internship Registration System using a Study Plan and Acceptance Letter Based on Tesseract-OCR and Template Matching

Synchronization of the Internship Registration System using a Study Plan and Acceptance Letter Based on Tesseract-OCR and Template Matching

Development of an NLP-Based Automatic Data Retrieval Model

Information extraction from Visually Rich Documents using graph convolutional network

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