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.
image-classification: The goal of this task is to classify a given document into one of 16 classes representing document types (letter, form, etc.). The leaderboard for this task is available here.All the classes and documents use English as their primary language.
A sample from the training set is provided below :
{
'image': <PIL.TiffImagePlugin.TiffImageFile image mode=L size=754x1000 at 0x7F9A5E92CA90>,
'label': 15
}
image: A PIL.Image.Image object containing a document.label: an int classification label.{
"0": "letter",
"1": "form",
"2": "email",
"3": "handwritten",
"4": "advertisement",
"5": "scientific report",
"6": "scientific publication",
"7": "specification",
"8": "file folder",
"9": "news article",
"10": "budget",
"11": "invoice",
"12": "presentation",
"13": "questionnaire",
"14": "resume",
"15": "memo"
}
| train | test | validation | |
|---|---|---|---|
| # of examples | 320000 | 40000 | 40000 |
The dataset was split in proportions similar to those of ImageNet.
From the paper:
This work makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis.
The same as in the IIT-CDIP collection.
The same as in the IIT-CDIP collection.
The same as in the IIT-CDIP collection.
The same as in the IIT-CDIP collection.
[More Information Needed]
[More Information Needed]
[More Information Needed]
[More Information Needed]
The dataset was curated by the authors - Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis.
RVL-CDIP is a subset of IIT-CDIP, which came from the Legacy Tobacco Document Library, for which license information can be found here.
@inproceedings{harley2015icdar,
title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval},
author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis},
booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}},
year = {2015}
}
Thanks to @dnaveenr for adding this dataset.