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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: label |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 12173747703 |
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num_examples: 7224600 |
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- name: val |
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num_bytes: 1352108669.283 |
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num_examples: 802733 |
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- name: test |
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num_bytes: 1484450563.896 |
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num_examples: 891924 |
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download_size: 12115256620 |
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dataset_size: 15010306936.179 |
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task_categories: |
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- image-to-text |
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language: |
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- en |
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size_categories: |
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- 1M<n<10M |
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pretty_name: MJSynth |
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--- |
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# Dataset Card for "MJSynth_text_recognition" |
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This is the MJSynth dataset for text recognition on document images, synthetically generated, covering 90K English words. |
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It includes training, validation and test splits. |
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Source of the dataset: https://www.robots.ox.ac.uk/~vgg/data/text/ |
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Use dataset streaming functionality to try out the dataset quickly without downloading the entire dataset (refer: https://huggingface.co/docs/datasets/stream) |
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Citation details provided on the source website (if you use the data please cite): |
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@InProceedings{Jaderberg14c, |
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author = "Max Jaderberg and Karen Simonyan and Andrea Vedaldi and Andrew Zisserman", |
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title = "Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition", |
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booktitle = "Workshop on Deep Learning, NIPS", |
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year = "2014", |
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} |
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@Article{Jaderberg16, |
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author = "Max Jaderberg and Karen Simonyan and Andrea Vedaldi and Andrew Zisserman", |
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title = "Reading Text in the Wild with Convolutional Neural Networks", |
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journal = "International Journal of Computer Vision", |
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number = "1", |
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volume = "116", |
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pages = "1--20", |
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month = "jan", |
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year = "2016", |
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} |