Novel Adaptive Binarization Method for Degraded Document Images

Achieving a good recognition rate for degraded document images is difficult as degraded document images suffer from low contrast, bleed-through, and nonuniform illumination effects. Unlike the existing baseline thresholding techniques that use fixed thresholds and windows, the proposed method introduces a concept for obtaining dynamic windows according to the image content to achieve better binarization. To enhance a low-contrast image, we proposed a new mean histogram stretching method for suppressing noisy pixels in the background and, simultaneously, increasing pixel contrast at edges or near edges, which results in an enhanced image. For the enhanced image, we propose a new method for deriving adaptive local thresholds for dynamic windows. The dynamic window is derived by exploiting the advantage of Otsu thresholding. To assess the performance of the proposed method, we have used standard databases, namely, document image binarization contest (DIBCO), for experimentation. The comparative study on well-known existing methods indicates that the proposed method outperforms the existing methods in terms of quality and recognition rate.