InfographicVQA

Mathew, Minesh; Bagal, Viraj; Tito, Rubèn; Karatzas, Dìmosthenis; Valveny, Ernest; Jawahar, C. V.

doi:10.1109/wacv51458.2022.00264

Cited by 50 publications

(38 citation statements)

References 19 publications

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“…BROS was proposed with an effective pretraining method (i.e., area masking) and a relative positional encoding trick. To validate the effectiveness of Webvicob-generated data, we pretrain BROS and measure performance on DocVQA Task 1 (Tito et al, 2021) and Task 3 (Mathew et al, 2022).…”

Section: Comparison Methodsmentioning

confidence: 99%

Technical Report on Web-based Visual Corpus Construction for Visual Document Understanding

Kim¹,

Hong²,

Yim³

et al. 2022

Preprint

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We present a dataset generator engine named Web-based Visual Corpus Builder (Webvicob). Webvicob can readily construct a large-scale visual corpus (i.e., images with text annotations) from a raw Wikipedia HTML dump. In this report, we validate that Webvicobgenerated data can cover a wide range of context and knowledge and helps practitioners to build a powerful Visual Document Understanding (VDU) backbone. The proposed engine is publicly available at https:// github.com/clovaai/webvicob.

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Section: Comparison Methodsmentioning

confidence: 99%

Technical Report on Web-based Visual Corpus Construction for Visual Document Understanding

Kim¹,

Hong²,

Yim³

et al. 2022

Preprint

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“…Document Intelligence can be considered as an umbrella term covering problems of Key Information Extraction [10,54], Table Detection [41,38] and Structure Recognition [39,55], Document Layout Segmentation [5,4] Document Layout Generation [6,36,3,48], Document Visual Question Answering [51,50,32], Document Image Enhancement [49,22,47] which involves the understanding of visually rich semantic information and structure of different layout entities of a whole page.…”

Section: Related Workmentioning

confidence: 99%

OCR-IDL: OCR Annotations for Industry Document Library Dataset

Biten¹,

Tito²,

Gómez³

et al. 2022

Preprint

Self Cite

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Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in https://github.com/furkanbiten/idl_data.

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“…VQA for documents first appears in the DocVQA dataset (Mathew et al, 2021b), which contains more than 12,000 documents and corresponding 5,000 questions. Later, InfographicVQA (Mathew et al, 2021a) is also proposed, which is a VQA benchmark for infographic images in the documents.…”

Section: Visual Information Extractionmentioning

confidence: 99%

Document AI: Benchmarks, Models and Applications

Cui¹,

Xu²,

Lv³

et al. 2021

Preprint

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Document AI, or Document Intelligence, is a relatively new research topic that refers to the techniques for automatically reading, understanding, and analyzing business documents. It is an important research direction for natural language processing and computer vision. In recent years, the popularity of deep learning technology has greatly advanced the development of Document AI, such as document layout analysis, visual information extraction, document visual question answering, document image classification, etc. This paper briefly reviews some of the representative models, tasks, and benchmark datasets. Furthermore, we also introduce early-stage heuristic rule-based document analysis, statistical machine learning algorithms, and deep learning approaches especially pre-training methods. Finally, we look into future directions for Document AI research.

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InfographicVQA

Cited by 50 publications

References 19 publications

Technical Report on Web-based Visual Corpus Construction for Visual Document Understanding

Technical Report on Web-based Visual Corpus Construction for Visual Document Understanding

OCR-IDL: OCR Annotations for Industry Document Library Dataset

Document AI: Benchmarks, Models and Applications

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