Beschreibung: In this paper, we present a new and freely available dataset comprising 80 pages of an historical handwritten Arabic document in conjunction with a detailed ground truth for the development and evaluation of segmentation-free word spotting approaches. Besides information on the underlying manuscript and technical details, we introduce a comprehensive list of tags that each word is labeled with. These tags can be used for research on specific issues such as dealing with text in different colors. For comparison of different word spotters, a fixed set of 25 keywords with different properties is included. Furthermore, some specifics of spotting on Arabic manuscripts are discussed. We exemplarily present a state-of-the-art word spotting algorithm in its original and a new  extended implementation and evaluate both approaches on the new dataset.   For comparison, they are also tested on the widely used George Washington dataset. It is shown that the extended word spotter outperforms the original version in terms of mean average precision on both datasets.

Beschreibung: Binarization is often used for pixel-wise document text extraction as preprocessing step for scanned historical documents. These documents are scanned in color and high resolution today. The reduction of color to grayscale images and the subsequent binarization implies a loss of information and often results in unsatisfying processing results. In this paper, a color segmentation instead of a binarization approach is used to segment text from background in historical manuscripts. A color segmentation approach based on Markov random fields with a reduced set of required parameters is presented to segment text written in different colors from noisy page background. First tests with historical Arabic manuscripts show promising results. In case of words written in light red color, our approach shows better results than a state-of-the-art binarization approach.