CORD: A Consolidated Receipt Dataset for Post-OCR Parsing

By Seunghyun Park, Seung Shin, Bado Lee, Junyeop Lee, Jaeheung Surh, Minjoon Seo, Hwalsuk Lee. 2019

336 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c69942bf1b4f75e53cb62d0c5126c1cb4a5aa7bc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yupan Huang
2. Sangdoo Yun
3. Geewook Kim
4. Tengchao Lv
5. Lei Cui
6. Furu Wei
7. Teakgyu Hong
8. Yang Xu

Citations in the corpus

• 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]).”
• LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking (2022, methodology): “We evaluated pre-trained LayoutLMv3 models across five public benchmarks, including text-centric benchmarks: FUNSD [19] for form understanding, CORD [35] for receipt understanding, DocVQA [34] for document visual question answering, and image-centric benchmarks: RVL-CDIP [15] for document image clas”
• 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.”
• 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.”
• OCRBench: on the hidden mystery of OCR in large multimodal models (2023, unknown): “Despite its notable success across various datasets and settings, ICL-D3IE encounters challenges in the in-domain setting on CORD [103].”
• OCR-Free Document Understanding Transformer (2021, methodology): “The Consolidated Receipt Dataset (CORD) 3 [45] is a public benchmark that consists of 0.8K train, 0.1K valid, 0.1K test receipt images.”