{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:57:23Z","timestamp":1777705043269,"version":"3.51.4"},"reference-count":17,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,4,18]]},"abstract":"<jats:p>Due to intensified off-balance sheet disclosure by regulatory authorities, financial reports now contain a substantial amount of information beyond the financial statements. Consequently, the length of footnotes in financial reports exceeds that of the financial statements. This poses a novel challenge for regulators and users of financial reports in efficiently managing this information. Financial reports, with their clear structure, encompass abundant structured information applicable to information extraction, automatic summarization, and information retrieval. Extracting headings and paragraph content from financial reports enables the acquisition of the annual report text\u2019s framework. This paper focuses on extracting the structural framework of annual report texts and introduces an OpenCV-based method for text framework extraction using computer vision. The proposed method employs morphological image dilation to distinguish headings from the main body of the text. Moreover, this paper combines the proposed method with a traditional, rule-based extraction method that exploits the characteristic features of numbers and symbols at the beginning of headings. This combination results in an optimized framework extraction method, producing a more concise text framework.<\/jats:p>","DOI":"10.3233\/jifs-234170","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T11:18:59Z","timestamp":1708427939000},"page":"8089-8108","source":"Crossref","is-referenced-by-count":3,"title":["A text extraction framework of financial report in traditional format with OpenCV"],"prefix":"10.1177","volume":"46","author":[{"given":"Jiaxin","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Finance and Commerce, Qinghai Higher Vocational and Technical Institute, Haidong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Yang","sequence":"additional","affiliation":[{"name":"Office of Academic Research, Qinghai Higher Vocational and Technical Institute, Haidong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Finance and Commerce, Qinghai Higher Vocational and Technical Institute, Haidong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/JIFS-234170_ref1","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.32604\/cmc.2021.014361","article-title":"Automatic Persian text summarization using linguistic features from text structure analysis","volume":"69","author":"Heidary","year":"2021","journal-title":"Computers, Materials & Continua"},{"issue":"9","key":"10.3233\/JIFS-234170_ref2","doi-asserted-by":"crossref","first-page":"2773","DOI":"10.35940\/ijitee.I8997.078919","article-title":"Extractive research on summarization framework for extracted features","volume":"8","author":"Bansal","year":"2019","journal-title":"International Journal of Innovative Technology and Exploring Engineering"},{"issue":"2","key":"10.3233\/JIFS-234170_ref3","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1177\/1475921718757405","article-title":"Computer vision and deep learning-based data anomaly detection method for structural health monitoring","volume":"18","author":"Bao","year":"2018","journal-title":"Structural Health Monitoring"},{"key":"10.3233\/JIFS-234170_ref7","unstructured":"Sharadkumar J. and Suvarna K. , Morphological image processing, International Journal in IT & Engineering (5) (2015)."},{"key":"10.3233\/JIFS-234170_ref9","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.ins.2018.09.028","article-title":"L-fuzzy relational mathematical morphology based on adjoint triples","volume":"474","author":"Madrid","year":"2019","journal-title":"Information Sciences"},{"key":"10.3233\/JIFS-234170_ref10","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.optlastec.2018.07.045","article-title":"Local binary pattern metric-based multi-focus image fusion","volume":"110","author":"Yin","year":"2018","journal-title":"Optics and Laser Technology"},{"key":"10.3233\/JIFS-234170_ref11","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.neucom.2018.10.039","article-title":"A spatially constrained shifted asymmetric Laplace mixture model for the grayscale image segmentation","volume":"331","author":"Sun","year":"2018","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-234170_ref13","doi-asserted-by":"crossref","first-page":"149","DOI":"10.4028\/www.scientific.net\/AMM.615.149","article-title":"OpenCV-based automatic detection system for automobile meter","volume":"615","author":"Hao","year":"2014","journal-title":"Applied Mechanics and Materials"},{"issue":"3","key":"10.3233\/JIFS-234170_ref15","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/MRA.2009.933612","article-title":"Learning OpenCV-computer vision with the Opencv library","volume":"16","author":"Zelinsky","year":"2009","journal-title":"IEEE Robotics & Automation Magazine"},{"key":"10.3233\/JIFS-234170_ref17","doi-asserted-by":"crossref","unstructured":"Malathi T. , Selvamuthukumaran D. , Diwaan Chandar C.S. , et al. 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