Computationally Efficient Implementation of Convolution-based Locally Adaptive Binarization Techniques

TL;DR

This paper introduces a more computationally efficient implementation of convolution-based locally adaptive binarization for document images, significantly reducing processing time while maintaining performance.

Contribution

It presents a novel implementation that reduces computational complexity from O(W^2 N^2) to O(W N^2), enabling faster processing on limited devices.

Findings

Computation time reduced by 5 to 15 times

Memory consumption remains unchanged

Performance comparable to original methods

Abstract

One of the most important steps of document image processing is binarization. The computational requirements of locally adaptive binarization techniques make them unsuitable for devices with limited computing facilities. In this paper, we have presented a computationally efficient implementation of convolution based locally adaptive binarization techniques keeping the performance comparable to the original implementation. The computational complexity has been reduced from O(W2N2) to O(WN2) where WxW is the window size and NxN is the image size. Experiments over benchmark datasets show that the computation time has been reduced by 5 to 15 times depending on the window size while memory consumption remains the same with respect to the state-of-the-art algorithmic implementation.