지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.11 No.5
- 발행연도
- 2022.10
- 수록면
- 316 - 323 (8page)
- DOI
- 10.5573/IEIESPC.2022.11.5.316
이용수
초록· 키워드
오류제보하기
Information extraction automatically obtains structured information from unstructured or semi-structured machine-readable documents. The extraction steps consist mainly of classifying words (tagging). The output can be stored as key-value pairs in a computer-friendly file format, and then stored in a database for later reference. Information extraction from receipts or invoices is a difficult task because the tagging step should not be done solely on machine-readable words. Also, we obtain layout information or positions of words relative to other words in the invoices or receipts. This study deployed optical character recognition solutions for the Vietnamese language (VietOCR) combining a graph convolutional network (GCN) to extract information from 731 Vietnamese invoices issued by several stores. First, we collected invoice images captured with smartphones from supermarkets in Vietnam. Then, with those images we proceeded with text detection and recognition, then feature processing. The dataset was classified into two parts for training and testing, and we executed classification tasks with two GCNs. Experimental results revealed that our proposed method reached 99.50%, 98.52%, 98.52%, and 98.52% for accuracy, recall, precision, and F1-score, respectively. This work is expected to prove useful for information extraction from image-based documents.
목차
Abstract
1. Introduction
2. Related Work
3. The Proposed Method
4. EXPERIMENTS
5. Conclusion
References
참고문헌 (27)
참고문헌 신청
M. Kerroumi, O. Sayem, and A. Shabou, “VisualWordGrid: Information Extraction from Scanned Documents Using a Multimodal Approach,” in Document Analysis and Recognition - ICDAR 2021 Workshops, Springer International Publishing, 2021, pp. 389-402. Article (CrossRef Link)
A. R. Katti et al., “Chargrid: Towards Understanding 2D Documents.” arXiv, 2018. Article (CrossRef Link)
T. I. Denk, “Wordgrid: Extending Chargrid with Word-level Information,” 2019, Article (CrossRef Link)
S. P. F. Joan and S. Valli, “A Survey on Text Information Extraction from Born-Digital and Scene Text Images,” Proc. Natl. Acad. Sci. India Sect. Phys. Sci., vol. 89, no. 1, pp. 77-101, Jan. 2018, Article (CrossRef Link)
K. Adnan and R. Akbar, “An analytical study of information extraction from unstructured and multidimensional big data,” J. Big Data, vol. 6, no. 1, Oct. 2019, Article (CrossRef Link)
A. R. Katti et al., “Chargrid: Towards Understanding 2D Documents.” arXiv, 2018. Article (CrossRef Link)
T. I. Denk, “Wordgrid: Extending Chargrid with Word-level Information,” 2019, Article (CrossRef Link)
S. P. F. Joan and S. Valli, “A Survey on Text Information Extraction from Born-Digital and Scene Text Images,” Proc. Natl. Acad. Sci. India Sect. Phys. Sci., vol. 89, no. 1, pp. 77-101, Jan. 2018, Article (CrossRef Link)
K. Adnan and R. Akbar, “An analytical study of information extraction from unstructured and multidimensional big data,” J. Big Data, vol. 6, no. 1, Oct. 2019, Article (CrossRef Link)
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