Datasets:
image imagewidth (px) 1 799 | label stringlengths 1 23 |
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Lube | |
Spencerian | |
accommodatingly | |
CARPENTER | |
REGURGITATING | |
savannas | |
unfix | |
CAGOULES | |
TRANSITS | |
looped | |
cowmen | |
SYSTEMICALLY | |
Offstages | |
Enquirers | |
pluck | |
FURLONG | |
Toked | |
Brawl | |
lancets | |
awarded | |
vaxes | |
CRANIUMS | |
UNBROKEN | |
REIT | |
Jarrod | |
UNFEIGNED | |
REGULATE | |
COLT | |
snooping | |
Marquise | |
SHELF | |
untasted | |
overusing | |
adaption | |
MENES | |
SILTIEST | |
KNURLING | |
SHOPFITTING | |
Ideas | |
outwitted | |
BIOL | |
Penmanship | |
SUSTAINED | |
HISTORICALLY | |
BORGLUM | |
PLAZAS | |
Contentment | |
callas | |
Banyan | |
randomized | |
populace | |
DEPORT | |
docked | |
GRASSROOTS | |
turbaned | |
Attired | |
Latches | |
Leisureliness | |
quenchless | |
frontbenches | |
Graffito | |
panderer | |
ENRICHED | |
CONQUERING | |
REINSPECTS | |
stickies | |
PRIVIEST | |
FEEDBAGS | |
DEMONETIZING | |
Stamina | |
libretto | |
Turtledove | |
Tongued | |
semitones | |
DEPRECATION | |
monopolizes | |
Temptingly | |
futon | |
PROVERB | |
Gu | |
floss | |
Mamacitas | |
Wisecracking | |
Fleece | |
Stupors | |
centavos | |
haunch | |
Realest | |
Reforests | |
fainted | |
Pests | |
detector | |
cup | |
Tojo | |
schwinn | |
SERIALIZE | |
Mckenzie | |
ANTIABORTION | |
trustworthy | |
DOGGED |
Dataset Card for "MJSynth_text_recognition"
This is the MJSynth dataset for text recognition on document images, synthetically generated, covering 90K English words. It includes training, validation and test splits. Source of the dataset: https://www.robots.ox.ac.uk/~vgg/data/text/
Use dataset streaming functionality to try out the dataset quickly without downloading the entire dataset (refer: https://huggingface.co/docs/datasets/stream)
Citation details provided on the source website (if you use the data please cite):
@InProceedings{Jaderberg14c, author = "Max Jaderberg and Karen Simonyan and Andrea Vedaldi and Andrew Zisserman", title = "Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition", booktitle = "Workshop on Deep Learning, NIPS", year = "2014", }
@Article{Jaderberg16, author = "Max Jaderberg and Karen Simonyan and Andrea Vedaldi and Andrew Zisserman", title = "Reading Text in the Wild with Convolutional Neural Networks", journal = "International Journal of Computer Vision", number = "1", volume = "116", pages = "1--20", month = "jan", year = "2016", }
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