Abstract

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 LBP image. Then, these features are used within a threshold-based method. Another variant is computed by combining a contrast information with the LBP operator to overcome the drawback caused by the poor contrasted document images. Experimental results conducted on DIBCO datasets and compared against some state-of-the-art methods, prove the effective use of the LBP for binarizing historical documents.

Keywords:
Local binary patterns Artificial intelligence Pixel Historical document Computer science Pattern recognition (psychology) Image (mathematics) Image texture Computer vision Contrast (vision) Binary image Histogram Image segmentation Image processing

Metrics

4
Cited By
0.21
FWCI (Field Weighted Citation Impact)
27
Refs
0.61
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Handwritten Text Recognition Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image Retrieval and Classification Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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