A Quality and Time Assessment of Binarization Algorithms

Lins, Rafael Dueire; Bernardino, Rodrigo Barros; Jesus, Darlisson Marinho de

doi:10.1109/icdar.2019.00232

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…The experimental verification of the proposed combined image binarization method for the OCR purposes should be conducted using a database of unevenly illuminated document images, for which the ground truth text data are known. Unfortunately, currently available image databases, such as the DIBCO [4], Bickley Diary [90], or Nabuco datasets [87], used for the performance analysis of image binarization methods contain usually a handwritten text (in some cases, also machine-printed) subjected to some distortions such as ink fading, the presence of some stains, or some other local distortions.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

“…Apart from the approaches presented during the recent ICDAR [87], some initial attempts at the use of multiple binarization methods were made by Chaki et al [6], as well as Yoon et al [88], although the presented results were obtained for a limited number of test images taken from earlier DIBCO datasets or captured images of vehicles' license plates. The idea of the combination of various image binarization based on pixel voting presented in this paper was verified using the 176 non-uniformly illuminated document images containing various kinds of illumination gradients, as well as five common font families, also with additional style modifications (bold, italics, and both of them) and utilized the combination of recently proposed methods with some adaptive binarization algorithms proposed earlier, based on different assumptions.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Robust Combined Binarization Method of Non-Uniformly Illuminated Document Images for Alphanumerical Character Recognition

Michalak

Okarma

2020

Sensors

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Image binarization is one of the key operations decreasing the amount of information used in further analysis of image data, significantly influencing the final results. Although in some applications, where well illuminated images may be easily captured, ensuring a high contrast, even a simple global thresholding may be sufficient, there are some more challenging solutions, e.g., based on the analysis of natural images or assuming the presence of some quality degradations, such as in historical document images. Considering the variety of image binarization methods, as well as their different applications and types of images, one cannot expect a single universal thresholding method that would be the best solution for all images. Nevertheless, since one of the most common operations preceded by the binarization is the Optical Character Recognition (OCR), which may also be applied for non-uniformly illuminated images captured by camera sensors mounted in mobile phones, the development of even better binarization methods in view of the maximization of the OCR accuracy is still expected. Therefore, in this paper, the idea of the use of robust combined measures is presented, making it possible to bring together the advantages of various methods, including some recently proposed approaches based on entropy filtering and a multi-layered stack of regions. The experimental results, obtained for a dataset of 176 non-uniformly illuminated document images, referred to as the WEZUT OCR Dataset, confirm the validity and usefulness of the proposed approach, leading to a significant increase of the recognition accuracy.

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Section: Discussion Of the Resultsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Robust Combined Binarization Method of Non-Uniformly Illuminated Document Images for Alphanumerical Character Recognition

Michalak

Okarma

2020

Sensors

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show abstract

“…In many methods the additional background removal, median filtering or morphological processing are required, as well as time-consuming training process for recently popular deep convolutional neural networks. Therefore, our moti-vation is the increase of performance of some classical methods due to efficient image preprocessing rather than comparison with sophisticated state-of-the-art methods and solutions based on deep learning, considering also the time-quality efficiency challenges [14].…”

Section: Introductionmentioning

confidence: 99%

Improved Two-Step Binarization of Degraded Document Images Based on Gaussian Mixture Model

Krupiński

Lech

Okarma

2020

Lecture Notes in Computer Science

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Image binarization is one of the most relevant preprocessing operations influencing the results of further image analysis conducted for many purposes. During this step a significant loss of information occurs and the use of inappropriate thresholding methods may cause difficulties in further shape analysis or even make it impossible to recognize different shapes of objects or characters. Some of the most typical applications utilizing the analysis of binary images are Optical Character Recognition (OCR) and Optical Mark Recognition (OMR), which may also be applied for unevenly illuminated natural images, as well as for challenging degraded historical document images, considered as typical benchmarking tools for image binarization algorithms.To face the still valid challenge of relatively fast and simple, but robust binarization of degraded document images, a novel two-step algorithm utilizing initial thresholding, based on the modelling of the simplified image histogram using Gaussian Mixture Model (GMM) and the Monte Carlo method, is proposed in the paper. This approach can be considered as the extension of recently developed image preprocessing method utilizing Generalized Gaussian Distribution (GGD), based on the assumption of its similarity to the histograms of ground truth binary images distorted by Gaussian noise. The processing time of the first step, producing the intermediate images with partially removed background information, may be significantly reduced due to the use of the Monte Carlo method.The proposed improved approach leads to even better results, not only for well-known DIBCO benchmarking databases, but also for more demanding Bickley Diary dataset, allowing the use of some well-known classical binarization methods, including the global ones, in the second step of the algorithm.

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Preprocessing of Document Images Based on the GGD and GMM for Binarization of Degraded Ancient Papyri Images

Michalak

Krupiński

Lech

et al. 2021

Progress in Image Processing, Pattern Recognition and Communication Systems

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A Quality and Time Assessment of Binarization Algorithms

Cited by 9 publications

References 31 publications

Robust Combined Binarization Method of Non-Uniformly Illuminated Document Images for Alphanumerical Character Recognition

Robust Combined Binarization Method of Non-Uniformly Illuminated Document Images for Alphanumerical Character Recognition

Improved Two-Step Binarization of Degraded Document Images Based on Gaussian Mixture Model

Preprocessing of Document Images Based on the GGD and GMM for Binarization of Degraded Ancient Papyri Images

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