Abstract
In this paper, a superpixel based segmentation of Historical Document Images (HDIs) using multiscale texture analysis is proposed. A Simple Linear Iterative Clustering (SLIC) superpixel technique and Kmeans classifier are applied in order to separate the input image into background and foreground superpixels. The foreground superpixels are characterized by the standard deviation and the mean of the Gabor features. These features are extracted in a multiscale fashion to adapt to the variability of the textures that may be present in HDIs. Text/graphic separation is then performed by applying a classification of the foreground superpixels for each texture analysis scale followed by a merging step of the obtained classification results. Since the classification results depend on the used classifier, a comparative study is performed for supervised (Support Vector Machine (SVM), K-Nearest Neighbors (KNN)) and unsupervised (Kmeans, Fuzzy C-Means (FCM)) techniques. Experiments show the effectiveness of our proposed method especially when compared with similar work in the literature.
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Soyed, E., Chaieb, R., Kalti, K. (2020). Superpixel Based Segmentation of Historical Document Images Using a Multiscale Texture Analysis. In: Bouhlel, M., Rovetta, S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1. SETIT 2018. Smart Innovation, Systems and Technologies, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-030-21005-2_33
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