1
Master Student of Computer Engineering at Shahid Bahonar University of Kerman
2
Dept. of Engineering, Shahid Bahonar University of Kerman
Abstract
Document images segmentation is one of the recent activities that have attracted researchers' attention. Unfortunately, there is no report on a benchmark dataset for Farsi document images understanding and segmentations applications that be available in the web. In the current article, a benchmark image dataset for the sake of the Farsi document images segmentation is presented, which includes 5598 images. The provided images are taken from the newspapers, textbooks and academic articles. Objects in the images are categorized and labeled into six different groups to be used easily in the subsequent applications. The object groups used in the dataset are paragraph(text), figure, table, logo, mathematical equation and header. To asset the effectiveness of the proposed document image dataset, three existing well-known methods based on deep learning are implemented on it and the results are presented.
Faraji, A., Saeed, M., & Nezamabadi-pour, H. (2023). Introducing a database for Farsi document image understanding and segmentation. Journal of Machine Vision and Image Processing, 10(2), 31-46.
MLA
Amin Faraji; Masoud Saeed; Hossein Nezamabadi-pour. "Introducing a database for Farsi document image understanding and segmentation". Journal of Machine Vision and Image Processing, 10, 2, 2023, 31-46.
HARVARD
Faraji, A., Saeed, M., Nezamabadi-pour, H. (2023). 'Introducing a database for Farsi document image understanding and segmentation', Journal of Machine Vision and Image Processing, 10(2), pp. 31-46.
VANCOUVER
Faraji, A., Saeed, M., Nezamabadi-pour, H. Introducing a database for Farsi document image understanding and segmentation. Journal of Machine Vision and Image Processing, 2023; 10(2): 31-46.
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