Table Information Extraction Using Data Augmentation on Deep Learning and Image Processing

Zulkarnain, Izuardo; Nurmalasari, Rin Rin; Azizah, Fazat Nur

doi:10.1109/tssa56819.2022.10063909

Cited by 1 publication

(1 citation statement)

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“…Another approach is to use machine learning techniques to learn representations of entities and relationships in a knowledge graph directly from the text and image data (Liu et al, 2020). Also, it is possible to use image processing techniques to extract information from figures and tables (Zulkarnain et al, 2022). For example, optical character recognition (OCR) can be used to extract text from figures, while computer vision techniques can be used to identify patterns and relationships in the data.…”

Section: State Of the Artmentioning

confidence: 99%

T2KG: Transforming Multimodal Document to Knowledge Graph

Galiano,

Muñoz,

Gutiérrez

et al. 2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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The large amount of textual information, in digital format available today, makes the knowledge extraction task unfeasible by manual means. It is therefore necessary to develop automatic tools that allow us to integrate this knowledge into a structure that is easy to use by both machines and humans. This paper presents T2KG, a framework that can incorporate the relevant information from several structured or unstructured documents into a semantic network. Structured documents are processed based on their annotation scheme. For unstructured documents, T2KG uses a set of Natural Language Processing sensors that identify relevant information to enrich the semantic network created by linking all the knowledge from different documents.

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Section: State Of the Artmentioning

confidence: 99%