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Binarization of ancient degraded document images is a very important step for their preservation and digital use. In this paper, a new simple threshold-based method is proposed for binarization of ancient degraded documents. The proposed method is inspired from the most popular threshold-based methods by exploiting texture information features extracted from both the filtered image using the Gabor...
Most of the classical threshold-based methods for document image binarization use simple features carried out from the spatial pixels values of the document images. In this paper, we present a new binarization method for degraded documents, based on Local Binary Pattern (LBP) as a texture measure. The mean and variance of pixels are computed respectively from both the original document image and the...
Since 2009, DIBCO is becoming the main benchmarking for evaluating objectively the performance of binarization methods by means of various quantitative measures. The usual evaluation protocol is performed on blind datasets, which contain degradations with unknown occurrence and variety. This leads to generate every year random ranking depending on the used dataset. This paper aims to propose a comparative...
Most of the classical methods for degraded document binarization are based on the pixel gray level intensity or on simple pixel neighborhood information such as mean or variance to compute the binarization threshold. Moreover, these information are extracted from the spatial domain of the document image which are not very discriminative. In this paper, we propose to estimate texture information based...
In this paper, we present a promising method for binarization of historical and degraded document images, based on texture features. The proposed method is an adaptive threshold-based. This latter is computed by using a descriptor based on a co-occurrence matrix. The proposed method is tested objectively, using DIBCO dataset degraded documents and subjectively, using a set of ancient degraded documents...
In this paper we propose a system for word spotting in Arabic historical document using Ridgelet transform and Dynamic Time Warping (DTW). First, a preprocessing and segmentation processes are applied to all document pages to create a word image dataset. Keeping each word into its original size, Ridgelet descriptor is generated without applying the normalization criteria for Radon transform, where...
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