Publication
The IUPR Dataset of Camera-Captured Document Images
Syed Saqib Bukhari; Faisal Shafait; Thomas Breuel
In: 4th International Workshop on Camera-Based Document Analysis and Recognition. International Workshop on Camera-Based Document Analysis and Recognition (CBDAR-11), 4th, September 22, Beijing, China, Lecture Notes in Computer Science (LNCS), Springer, 9/2011.
Abstract
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 nontechnical
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 cameracaptured
document processing algorithms in order to solve the
challenging problems in the dataset and to compare different
methods on a common ground.