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In document image analysis (DIA) especially in handwritten document recognition, standard databases play significant roles for evaluating performances of algorithms and comparing results obtained by different groups of researchers. The field of DIA regard to Indo-Persian documents is still at its infancy compared to Latin script-based documents; as such standard datasets are not still available in literature. This paper is an effort towards alleviating this gap. In this paper, an unconstrained handwritten dataset containing documents of Persian, Bangla, Oriya and Kannada (PBOK) is introduced. The PBOK contains 707 text-pages written in four different languages (Persian, Bangla, Oriya and Kannada) by 436 individuals. Total number of text-lines, words/subwords and characters are 12,565, 104,541 and 423,980, respectively. In most documents of PBOK dataset contain either an overlapping or a touching text-lines. The average number of text-lines in text-pages of the PBOK dataset is 18. Two types of ground truths, based on pixels information and content information, are generated for the dataset. Because of such ground truths, the PBOK dataset can be utilized in many areas of document image processing e.g. text-line segmentation, word segmentation and word recognition. To provide an insight for other researches, recent text-line segmentation results on this dataset are also reported. [ABSTRACT FROM AUTHOR]

In document image processing, arc segmentation plays an important role in vectorization and graphic recognition. Moreover, the unsatisfactory results of several recent arc segmentation contests indicate that conventional methods are inadequate. This paper proposes a new arc segmentation algorithm called SymCAve (an acronym for Symmetry axis, Circle fitting and Average distribution points). First, we locate several seed points and adopt three strategies to ensure that the seed points are proper; then we calculate the center and radius utilizing the seed points. Second, the coordinates of the center and radius are adjusted by employing symmetry axes. Third, the average distribution points method is used to verify whether the points on the circumference are all black pixels. It is a complete circle if all of the points are black pixels. Otherwise, it is a partial circle if some of the points are black pixels and are continuous. Based on this information, the start and end angles of the partial circle can be determined. Finally, these arcs are verified to ensure that the results are accurate. Images and the evaluation tool were obtained from the GREC Workshop's Arc Segmentation contests, to test the systematic performance of the SymCAve algorithm. The experiments demonstrate that the proposed method can provide promising results. However, the algorithm has some drawbacks: it cannot detect a line with width of one pixel, small angles, and any large radius arcs. It is suited for segmenting images with appropriate symmetry axes. [ABSTRACT FROM AUTHOR]