The UvA color document dataset
International Journal on Document Analysis and Recognition
Document Image Dewarping using Robust Estimation of Curled Text Lines
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Google Book Search: Document Understanding on a Massive Scale
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Performance Evaluation and Benchmarking of Six-Page Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document cleanup using page frame detection
International Journal on Document Analysis and Recognition
Border noise removal of camera-captured document images using page frame detection
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
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Major challenges in camera-base document analysis are dealing with uneven shadows, high degree of curl and perspective distortions. In CBDAR 2007, we introduced the first dataset (DFKI-I) of camera-captured document images in conjunction with a page dewarping contest. One of the main limitations of this dataset is that it contains images only from technical books with simple layouts and moderate curl/skew. Moreover, it does not contain information about camera's specifications and settings, imaging environment, and document contents. This kind of information would be more helpful for understanding the results of the experimental evaluation of camera-based document image processing (binarization, page segmentation, dewarping, etc.). In this paper, we introduce a new dataset (the IUPR dataset) of camera-captured document images. As compared to the previous dataset, the new dataset contains images from different varieties of technical and non-technical books with more challenging problems, like different types of layouts, large variety of curl, wide range of perspective distortions, and high to low resolutions. Additionally, the document images in the new dataset are provided with detailed information about thickness of books, imaging environment and camera's viewing angle and its internal settings. The new dataset will help research community to develop robust camera-captured document processing algorithms in order to solve the challenging problems in the dataset and to compare different methods on a common ground.