ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction

By Zheng Huang, Kai Chen, Jianhua He, Xiang Bai, Dimosthenis Karatzas, Shijian Lu, C. V. Jawahar. IEEE International Conference on Document Analysis and Recognition, 2019

431 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d00cbb0c05c1dc922126fe72c1078b773d01c688

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Citations in the corpus

LVLM-EHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models (2023, neutral): “However, LVLMs that are pre-trained with relatively small pre-training datasets tend to struggle with image classiﬁcation, particularly for ﬁne-grained categories as depicted in Table VI, suffer from object hallucination as presented in Table IX.”
ImageBind-LLM: Multi-modality Instruction Tuning (2023, methodology): “For instance, in the case of the SROIE benchmark, prompts such as ”What is the name of the company that issued this invoice?” were used to extract company information, while prompts like ”Where was this invoice issued?” were employed to extract address information.”
Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models (2023, methodology): “88% on SROIE; in the out-of-distribution (OOD) setting, ICL-D3IE performs much better than previous pre-trained methods on all datasets.”
PP-OCR: A Practical Ultra Lightweight OCR System (2020, neutral): “2018), SROIE (Huang et al. 2019), MLT 2019 (Nayef et al.”
LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding (2020, methodology): “Entity Extraction Tasks Table 2 shows the model accuracy on the four datasets FUNSD, CORD, SROIE, and Kleister-NDA, which we regard as sequential labeling tasks evaluated using entity-level F1 score.”
WebSRC: A Dataset for Web-Based Structural Reading Comprehension (2021, background): “Jaume et al. (2019) proposed FUNSD for form understanding and Huang et al. (2019) organized SROIE competition for receipt understanding.”
Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning (2024, methodology): “We choose five text-related datasets to create data in this domain: TextVQA [53], DocVQA [43], DUDE [57], TextCaps [52], SROIE [17].”
DocFormer: End-to-End Transformer for Document Understanding (2021, background): “Document understanding methods in the literature have used various combinations of image, spatial and text features in order to understand and extract information from structurally rich documents such as forms [19, 59, 13], tables [46, 58, 25], receipts [28, 27] and invoices [36, 44, 39].”
Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models (2023, methodology): “In the paper, ICL-D3IE is compared with existing pre-trained models such as LiLT,BROS (in-distribution (ID) setting and out-of-distribution (OOD) setting) on datasets (FUNSD, CORD, and SROIE [64, 65, 66]).”
OCR-Free Document Understanding Transformer (2021, background): “There have been various visual document understanding (VDU) methods to understand and extract essential information from the semi-structured documents such as receipts [20,25,18], invoices [49], and form documents [14,6,43].”
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models (2025, neutral): “2 [124], which cover a wide range of domains, including general visual question answering (VQA) [43, 50, 90, 83, 127, 126], science [57, 16, 82], chart [91, 54, 11], mathematics [72, 104, 10, 81, 55, 40, 147, 106], OCR [92, 107, 9, 49, 96], and document [24].”
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling (2024, neutral): “…[112], SynthDoG-JP [112], SynthDoG-KO [112], IAM [180], EST-VQA [253], ST-VQA [17], NAF [52], InfoVQA [183], HME100K [288], OCRVQA [188], SROIE [97], POIE [115], CTW [287], SynthText [79], ArT [40], LSVT [222], RCTW-17 [209], ReCTs [301], MTWI [82], TextVQA [212], CASIA [146], TextOCR [213],…”