Shape based local thresholding for binarization of document images
Pattern Recognition Letters
An optimization for binarization methods by removing binary artifacts
Pattern Recognition Letters
A combined approach for the binarization of handwritten document images
Pattern Recognition Letters
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In this paper, a new document image binarization technique is presented, as an improved version of the state-of-the-art adaptive logical level technique (ALLT). The original ALLT depends on fixed windows to extract essential features such as the character stroke width. Since characters with several different stroke widths may exist within a region, this can lead to erroneous results. In our approach, we use local adaptive binarization as a guide to our adaptive stroke width detection. The skeleton and the contour points of the binarization output are combined to identify locally the stroke width. Additionally, we introduce an adaptive local parameter “β” that enhances the characters and improves the overall performance. In this way, we achieve more accurate binarization results in both handwritten and printed documents with a particular focus on degraded historical documents. Experimental results prove the effectiveness of the proposed technique compared to other state-of-the-art methodologies.