Information Extraction from Unstructured data using Augmented-AI and Computer Vision
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916862435000320 |
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| author | Parikh, Aditya |
| author_facet | Parikh, Aditya |
| contents | Information extraction (IE) from unstructured documents remains a critical challenge in data processing pipelines. Traditional optical character recognition (OCR) methods and conventional parsing engines demonstrate limited effectiveness when processing large-scale document datasets. This paper presents a comprehensive framework for information extraction that combines Augmented Intelligence (A2I) with computer vision and natural language processing techniques. Our approach addresses the limitations of conventional methods by leveraging deep learning architectures for object detection, particularly for tabular data extraction, and integrating cloud-based services for scalable document processing. The proposed methodology demonstrates improved accuracy and efficiency in extracting structured information from diverse document formats including PDFs, images, and scanned documents. Experimental validation shows significant improvements over traditional OCR-based approaches, particularly in handling complex document layouts and multi-modal content extraction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_09880 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Information Extraction from Unstructured data using Augmented-AI and Computer Vision Parikh, Aditya Computer Vision and Pattern Recognition Information extraction (IE) from unstructured documents remains a critical challenge in data processing pipelines. Traditional optical character recognition (OCR) methods and conventional parsing engines demonstrate limited effectiveness when processing large-scale document datasets. This paper presents a comprehensive framework for information extraction that combines Augmented Intelligence (A2I) with computer vision and natural language processing techniques. Our approach addresses the limitations of conventional methods by leveraging deep learning architectures for object detection, particularly for tabular data extraction, and integrating cloud-based services for scalable document processing. The proposed methodology demonstrates improved accuracy and efficiency in extracting structured information from diverse document formats including PDFs, images, and scanned documents. Experimental validation shows significant improvements over traditional OCR-based approaches, particularly in handling complex document layouts and multi-modal content extraction. |
| title | Information Extraction from Unstructured data using Augmented-AI and Computer Vision |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.09880 |