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The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels.

For questions and comments please contact Adam Harley (aharley@scs.ryerson.ca).

The full dataset can be found [here](https://www.cs.cmu.edu/~aharley/rvl-cdip/).

## Labels

 0: advertissement  
 1: budget   
 2: email   
 3: file folder   
 4: form   
 5: handwritten   
 6: invoice   
 7: letter   
 8: memo   
 9: news article   
10: presentation   
11: questionnaire   
12: resume   
13: scientific publication   
14: scientific report   
15: specification   

## Citation

This dataset is from this [paper](https://www.cs.cmu.edu/~aharley/icdar15/) `A. W. Harley, A. Ufkes, K. G. Derpanis, "Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval," in ICDAR, 2015`

## License

RVL-CDIP is a subset of IIT-CDIP, which came from the [Legacy Tobacco Document Library](https://www.industrydocuments.ucsf.edu/tobacco/), for which license information can be found [here](https://www.industrydocuments.ucsf.edu/help/copyright/).

## References

1. D. Lewis, G. Agam, S. Argamon, O. Frieder, D. Grossman, and J. Heard, "Building a test collection for complex document information processing," in Proc. 29th Annual Int. ACM SIGIR Conference (SIGIR 2006), pp. 665-666, 2006
2. The Legacy Tobacco Document Library (LTDL), University of California, San Francisco, 2007. http://legacy.library.ucsf.edu/.
