Optical character recognition using deep learning for keyword-triggered value extraction in documents

The manual labour hours needed to compare values within documents remain one of the top inefficiencies that Manufacturing companies like SLB face when performing their regular Quality Control Inspection. On top of that, the risks of errors are high due to its manual handling nature. To combat th...

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Main Author: Lie, Valencia
Other Authors: Lee Bu Sung, Francis
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/174994
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1749942024-04-19T15:41:52Z Optical character recognition using deep learning for keyword-triggered value extraction in documents Lie, Valencia Lee Bu Sung, Francis School of Computer Science and Engineering SLB EBSLEE@ntu.edu.sg Computer and Information Science The manual labour hours needed to compare values within documents remain one of the top inefficiencies that Manufacturing companies like SLB face when performing their regular Quality Control Inspection. On top of that, the risks of errors are high due to its manual handling nature. To combat these problems, this research project aims to use automation and Machine Learning to extract meaningful key-value pairs within documents and compare them automatically. This is done in three different sections: extraction of texts from images using OCR engines, extraction of key-value pairs using Layout-based models and the linking of key-value pairs using Graph-based models and Proximity-based algorithm. On top of these three segments, a prototype is also developed to showcase the modules working hand-in-hand. Although there are past research projects that aim to tackle similar issues, most of the research projects only focus on one of the aspects mentioned above, instead of tackling the problem end-to-end. Furthermore, limited attention has been given to the manufacturing industry in this specific domain as other research projects mainly focus on documents from other industries, such as healthcare documents and retail receipts. While not fully commercially ready, the findings and development detailed in this research project impart valuable knowledge on how to tackle the issue at hand as it addresses multiple facets of the problem. Bachelor's degree 2024-04-19T02:23:33Z 2024-04-19T02:23:33Z 2024 Final Year Project (FYP) Lie, V. (2024). Optical character recognition using deep learning for keyword-triggered value extraction in documents. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/174994 https://hdl.handle.net/10356/174994 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
spellingShingle Computer and Information Science
Lie, Valencia
Optical character recognition using deep learning for keyword-triggered value extraction in documents
description The manual labour hours needed to compare values within documents remain one of the top inefficiencies that Manufacturing companies like SLB face when performing their regular Quality Control Inspection. On top of that, the risks of errors are high due to its manual handling nature. To combat these problems, this research project aims to use automation and Machine Learning to extract meaningful key-value pairs within documents and compare them automatically. This is done in three different sections: extraction of texts from images using OCR engines, extraction of key-value pairs using Layout-based models and the linking of key-value pairs using Graph-based models and Proximity-based algorithm. On top of these three segments, a prototype is also developed to showcase the modules working hand-in-hand. Although there are past research projects that aim to tackle similar issues, most of the research projects only focus on one of the aspects mentioned above, instead of tackling the problem end-to-end. Furthermore, limited attention has been given to the manufacturing industry in this specific domain as other research projects mainly focus on documents from other industries, such as healthcare documents and retail receipts. While not fully commercially ready, the findings and development detailed in this research project impart valuable knowledge on how to tackle the issue at hand as it addresses multiple facets of the problem.
author2 Lee Bu Sung, Francis
author_facet Lee Bu Sung, Francis
Lie, Valencia
format Final Year Project
author Lie, Valencia
author_sort Lie, Valencia
title Optical character recognition using deep learning for keyword-triggered value extraction in documents
title_short Optical character recognition using deep learning for keyword-triggered value extraction in documents
title_full Optical character recognition using deep learning for keyword-triggered value extraction in documents
title_fullStr Optical character recognition using deep learning for keyword-triggered value extraction in documents
title_full_unstemmed Optical character recognition using deep learning for keyword-triggered value extraction in documents
title_sort optical character recognition using deep learning for keyword-triggered value extraction in documents
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/174994
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