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This paper proposes a technique for processing handwriting images. The algorithm used in this study is an improvement to the binarisation process. The enhancement focuses on correcting damaged lines that usually arise during the binarisation process, particularly, spurious holes, discontinuities, and eroded boundaries. The presented method uses a morphogical dilation operation in which a structural element is locally adapted using the information from a directional field. The adaptation process involves a new criterion for selecting orientation and shape of a structural element that combines directional field, a coherence measure, and a circular histogram. The field was computed using gradient-based approach, and a method based on a Hessian matrix. During experiments, our method was applied to the output of selected binarisation algorithms. The experiments were conducted on grayscale signature images (from the CEDAR database) and handwriting images (from the DIBCO database). The results of the algorithm were compared to the results of standard morphological operations (dilation, erosion, opening, and closing) and median filtering. The experiments show that the proposed method achieves significant accuracy improvement (8%–12% for Acc, 15%–32% for Acc2 measures), reduces the number of unwanted artefacts, and produces images with less distortion compared to those from standard approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a technique for processing handwriting images. The algorithm used in this study is an improvement to the binarisation process. The enhancement focuses on correcting damaged lines that usually arise during the binarisation process, particularly, spurious holes, discontinuities, and eroded boundaries. The presented method uses a morphogical dilation operation in which a structural element is locally adapted using the information from a directional field. The adaptation process involves a new criterion for selecting orientation and shape of a structural element that combines directional field, a coherence measure, and a circular histogram. The field was computed using gradient-based approach, and a method based on a Hessian matrix. During experiments, our method was applied to the output of selected binarisation algorithms. The experiments were conducted on grayscale signature images (from the CEDAR database) and handwriting images (from the DIBCO database). The results of the algorithm were compared to the results of standard morphological operations (dilation, erosion, opening, and closing) and median filtering. The experiments show that the proposed method achieves significant accuracy improvement (8%–12% for Acc, 15%–32% for Acc2 measures), reduces the number of unwanted artefacts, and produces images with less distortion compared to those from standard approaches. [ABSTRACT FROM AUTHOR]