A learning framework for the optimization and automation of document binarization methods

Cheriet, Mohamed; Moghaddam, Reza Farrahi; Hedjam, Rachid

doi:10.1016/j.cviu.2012.11.003

Cited by 33 publications

(20 citation statements)

References 29 publications

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“…In [21], a learning framework for the optimization of the binarization methods is introduced, which is designed to determine the optimal parameter values for a document image.…”

Section: Sauvola's Algorithm and Issuesmentioning

confidence: 99%

ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization

Hadjadj

Meziane

Cherfa

et al. 2016

Lecture Notes in Computer Science

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“…In [21], a learning framework for the optimization of the binarization methods is introduced, which is designed to determine the optimal parameter values for a document image.…”

Section: Sauvola's Algorithm and Issuesmentioning

confidence: 99%

ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization

Hadjadj

Meziane

Cherfa

et al. 2016

Lecture Notes in Computer Science

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“…In [26], a learning framework for the optimization of the binarization methods is introduced, which is designed to determine the optimal parameter values for a document image. The framework works with any binarization method performs three main steps: extracts features, estimates optimal parameters, and learns the relationship between features and optimal parameters.…”

Section: Recent Workmentioning

confidence: 99%

A Comprehensive Survey on Image Binarization Techniques

Chaki

Shaikh

Saeed

2014

Studies in Computational Intelligence

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“…Another approach to address the parameter tuning problem was taken by Cheriet et al [7], which is more closely related to our own approach. In their paper, Cheriet et al propose a machine learning framework to add automatic parameter tuning to any image binarization algorithm.…”

Section: Automatic Parameter Tuningmentioning

confidence: 99%

Efficient document image binarization using heterogeneous computing and parameter tuning

Westphal

Grahn

Lavesson

2018

IJDAR

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In the context of historical document analysis, image binarization is a first important step, which separates foreground from background, despite common image degradations, such as faded ink, stains, or bleed-through. Fast binarization has great significance when analyzing vast archives of document images, since even small inefficiencies can quickly accumulate to years of wasted execution time. Therefore, efficient binarization is especially relevant to companies and government institutions, who want to analyze their large collections of document images. The main challenge with this is to speed up the execution performance without affecting the binarization performance. We modify a state-of-the-art binarization algorithm and achieve on average a 3.5 times faster execution performance by correctly mapping this algorithm to a heterogeneous platform, consisting of a CPU and a GPU. Our proposed parameter tuning algorithm additionally improves the execution time for parameter tuning by a factor of 1.7, compared to previous parameter tuning algorithms. We see that for the chosen algorithm, machine learning-based parameter tuning improves the execution performance more than heterogeneous computing, when comparing absolute execution times.

show abstract

A learning framework for the optimization and automation of document binarization methods

Cited by 33 publications

References 29 publications

ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization

ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization

A Comprehensive Survey on Image Binarization Techniques

Efficient document image binarization using heterogeneous computing and parameter tuning

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