Computer Vision-Based Framework for Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach to Unlocking Financial Insights
Information extraction from financial document images is crucial in computer vision and NLP, as financial data often exists in image or PDF format, enabling organizations to analyze and make informed business decisions using OCR advancements. The table contents of financial document images are one o...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10813363/ |
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| author | Iftakhar Ali Khandokar Priya Deshpande |
| author_facet | Iftakhar Ali Khandokar Priya Deshpande |
| author_sort | Iftakhar Ali Khandokar |
| collection | DOAJ |
| description | Information extraction from financial document images is crucial in computer vision and NLP, as financial data often exists in image or PDF format, enabling organizations to analyze and make informed business decisions using OCR advancements. The table contents of financial document images are one of the prominent structures to confine important portions of data of the document and many Deep learning-based methods have been proposed to detect Table regions inside document images. The shortcomings of the current approach are that it is bounded within the detection of the table region and struggles in cases such as handling different layouts and preserving the relation among the different attributes of the table. Therefore, in this work, we proposed an end-to-end architecture to extract information from Financial table images while preserving the column row structures of the attributes within the table. We divided the task into four modules and generated synthesized data with different augmentation techniques to overcome data scarcity challenges and boost the performance of the pipeline modules. In terms of information extraction, the proposed method acquired 85% accuracy in the target invoice dataset. |
| format | Article |
| id | doaj-art-1e841ee628fc426a9d7ecab5ed4f28e3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1e841ee628fc426a9d7ecab5ed4f28e32025-01-31T00:02:05ZengIEEEIEEE Access2169-35362025-01-0113177061772310.1109/ACCESS.2024.352214110813363Computer Vision-Based Framework for Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach to Unlocking Financial InsightsIftakhar Ali Khandokar0https://orcid.org/0000-0002-7354-5736Priya Deshpande1https://orcid.org/0000-0002-2631-5751Department of Electrical and Computer Engineering (EECE), Marquette University, Milwaukee, WI, USADepartment of Electrical and Computer Engineering (EECE), Marquette University, Milwaukee, WI, USAInformation extraction from financial document images is crucial in computer vision and NLP, as financial data often exists in image or PDF format, enabling organizations to analyze and make informed business decisions using OCR advancements. The table contents of financial document images are one of the prominent structures to confine important portions of data of the document and many Deep learning-based methods have been proposed to detect Table regions inside document images. The shortcomings of the current approach are that it is bounded within the detection of the table region and struggles in cases such as handling different layouts and preserving the relation among the different attributes of the table. Therefore, in this work, we proposed an end-to-end architecture to extract information from Financial table images while preserving the column row structures of the attributes within the table. We divided the task into four modules and generated synthesized data with different augmentation techniques to overcome data scarcity challenges and boost the performance of the pipeline modules. In terms of information extraction, the proposed method acquired 85% accuracy in the target invoice dataset.https://ieeexplore.ieee.org/document/10813363/Computer visiondeep learninginformation extractiontransformer model |
| spellingShingle | Iftakhar Ali Khandokar Priya Deshpande Computer Vision-Based Framework for Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach to Unlocking Financial Insights IEEE Access Computer vision deep learning information extraction transformer model |
| title | Computer Vision-Based Framework for Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach to Unlocking Financial Insights |
| title_full | Computer Vision-Based Framework for Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach to Unlocking Financial Insights |
| title_fullStr | Computer Vision-Based Framework for Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach to Unlocking Financial Insights |
| title_full_unstemmed | Computer Vision-Based Framework for Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach to Unlocking Financial Insights |
| title_short | Computer Vision-Based Framework for Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach to Unlocking Financial Insights |
| title_sort | computer vision based framework for data extraction from heterogeneous financial tables a comprehensive approach to unlocking financial insights |
| topic | Computer vision deep learning information extraction transformer model |
| url | https://ieeexplore.ieee.org/document/10813363/ |
| work_keys_str_mv | AT iftakharalikhandokar computervisionbasedframeworkfordataextractionfromheterogeneousfinancialtablesacomprehensiveapproachtounlockingfinancialinsights AT priyadeshpande computervisionbasedframeworkfordataextractionfromheterogeneousfinancialtablesacomprehensiveapproachtounlockingfinancialinsights |