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- MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis
In this paper, we present a dataset MIDV-2020 which consists of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock identity documents, each with unique text field values and unique artificially generated faces, with rich annotation
- GitHub - fcakyon midv500: Download and convert MIDV-500 annotations to . . .
Automatically download unzip MIDV-500 and MIDV-2019 datasets and convert the annotations into COCO instance segmentation format Then, dataset can be directly used in the training of Yolact, Detectron type of models
- (PDF) MIDV-2020: A Comprehensive Benchmark Dataset for Identity . . .
In this paper, we present a dataset MIDV-2020 which consists of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock identity documents, each with unique text field values
- MIDV-500 Dataset - Papers With Code
In this paper we present a Mobile Identity Document Video dataset (MIDV-500) consisting of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide scope of document analysis problems
- MIDV-500: A Dataset for Identity Documents Analysis and Recognition on . . .
Video dataset (MIDV-500), which in contrast to other relevant publicly available datasets can be used to de-velop, demonstrate and benchmark a coherent processing pipeline of identity document analysis and recognition in its modern applications and use cases All source docu-ment images used in MIDV-500 are either in public do-
- MIDV-2019 Dataset - Papers With Code
midv-2019 Introduced by Bulatov et al in MIDV-2019: Challenges of the modern mobile-based document OCR Contains video clips shot with modern high-resolution mobile cameras, with strong projective distortions and with low lighting conditions
- (PDF) MIDV-500: a dataset for identity document analysis and . . .
We propose to examine the link between deep learning-based binarization and recognition algorithms for this sort of documents on the MIDV-500 and MIDV-2020 datasets
- MIDV-2019: Challenges of the modern mobile-based document OCR
In this paper we present a MIDV-2019 dataset, containing video clips shot with modern high-resolution mobile cameras, with strong projective distortions and with low lighting conditions The description of the added data is presented, and experimental baselines for text field recognition in different conditions
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