Datasets › MSDA

MSDA (Multi-source domain adaptation dataset for text recognition)

Introduced by Shuhao Qiu et al. in Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark24 Aug 2021 archive 2025-07-28

5 domains: synthetic domain, document domain, street view domain, handwritten domain, and car license domain
over five million images

Homepage Benchmarks Papers JSON

Benchmarks archive 2025-07-28

All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

		First row (archive order)	Paper	Code
Scene Text Recognition	MSDA	MetaSelf-Learning Average Accuracy 42%	Meta Self-Learning for Multi-Source Domain Adaptation: A...	bupt-ai-cz/Meta-SelfLearning	2	Compare
Domain Adaptation	MSDA	MetaSelf-Learning Average Accuracy 42%	Meta Self-Learning for Multi-Source Domain Adaptation: A...	bupt-ai-cz/Meta-SelfLearning	1	Compare

Papers archive 2025-07-28

2 shown of 2 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 2. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

			Date	Samples run Syntology
Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark	1	2	24 Aug 2021	not harvested
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond	2	1	5 Apr 2018	not harvested

Dataset loaders archive 2025-07-28

bupt-ai-cz/Meta-SelfLearningtfpytorch

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

Object Detection Domain Adaptation Unsupervised Domain Adaptation Optical Character Recognition (OCR)Scene Text Recognition License Plate Detection

License archive 2025-07-28

This Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms.

Modalities archive 2025-07-28

Images Texts

Languages archive 2025-07-28

English Chinese

Variants archive 2025-07-28

MSDA

1 variant name, as the archive lists them.

At a glance archive 2025-07-28

Introduced: 24 Aug 2021
Papers: 2
Benchmarks: 2
With a result: 2
Licence: This Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms.
Modalities: Images, Texts
Languages: English, Chinese
Loaders: 1
Homepage: bupt-ai-cz.github.io/Meta-SelfLearning/

Samples run Syntology

Of the 2 papers with a result here, 0 have harvested code samples in Syntology's graph. Summed over those: Syntology ran 0 of 0 samples; 0 unverified. 0 of the 0 have no sample that ran. Each row in the Papers table states its own "ran N of M samples" with the unverified count; a sample that ran executed on a synthesized fixture, which is not a correctness claim about any result. Read from the graph 2026-09-24.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections