ICDAR 2019 SROIE Task-3
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An ICDAR 2019 SROIE Task-3 is a Key Information Extraction from Scanned Receipts Benchmark Task.
References
2019
- (Zhao, Niu et al., 2019) ⇒ Xiaohui Zhao, Endi Niu, Zhuo Wu, and Xiaoguang Wang. (2019). “CUTIE: Learning to Understand Documents with Convolutional Universal Text Information Extractor.” In: arXiv preprint arXiv:1903.12363.
2019
- (Huang, Chen et al., 2019) ⇒ Zheng Huang, Kai Chen, Jianhua He, Xiang Bai, Dimosthenis Karatzas, Shijian Lu, and C. V . Jawahar. (2019). “Icdar2019 Competition on Scanned Receipt Ocr and Information Extraction.” In: The Proceedings of the 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1516-1520 . IEEE,
- ABSTRACT: The ICDAR 2019 Challenge on "Scanned receipts OCR and key information extraction" (SROIE) covers important aspects related to the automated analysis of scanned receipts. The SROIE tasks play a key role in many document analysis systems and hold significant commercial potential. Although a lot of work has been published over the years on administrative document analysis, the community has advanced relatively slowly, as most datasets have been kept private. One of the key contributions of SROIE to the document analysis community is to offer a first, standardized dataset of 1000 whole scanned receipt images and annotations, as well as an evaluation procedure for such tasks. The Challenge is structured around three tasks, namely Scanned Receipt Text Localization (Task 1), Scanned Receipt OCR (Task 2) and Key Information Extraction from Scanned Receipts (Task 3). The competition opened on 10th February, 2019 and closed on 5th May, 2019. We received 29, 24 and 18 valid submissions received for the three competition tasks, respectively. This report presents the competition datasets, define the tasks and the evaluation protocols, offer detailed submission statistics, as well as an analysis of the submitted performance. While the tasks of text localization and recognition seem to be relatively easy to tackle, it is interesting to observe the variety of ideas and approaches proposed for the information extraction task. According to the submissions' performance we believe there is still margin for improving information extraction performance, although the current dataset would have to grow substantially in following editions. Given the success of the SROIE competition evidenced by the wide interest generated and the healthy number of submissions from academic, research institutes and industry over different countries, we consider that the SROIE competition can evolve into a useful resource for the community, drawing further attention and promoting research and development efforts in this field.