Automatic Ground Truth Generation of Camera Captured Documents Using Document Image Retrieval

Sheraz Ahmed; Koichi Kise; Masakazu Iwamura; Marcus Liwicki; Andreas Dengel
In: ICDAR. International Conference on Document Analysis and Recognition (ICDAR-2013), August 25-28, Washington, DC, USA, IEEE, 2013.


In this paper a novel method for automatic ground truth generation of camera captured document images is proposed. Currently, no dataset is available for camera captured documents. It is very difficult to build these datasets manually, as it is very laborious and costly. The proposed method is fully automatic, allowing building the very large scale (i.e., millions of images) labeled camera captured documents dataset, without any human intervention. Evaluation of samples generated by the proposed approach shows that 99.98% of the images are correctly labeled. Novelty of the proposed approach lies in the use of document image retrieval for automatic labeling, especially for camera captured documents, which contain different distortions specific to camera, e.g., blur, occlusion, perspective distortion, etc.

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