ICDAR 2019 Time-Quality Binarization Competition

Lins, Rafael Dueire; Kavallieratou, Ergina; Smith, Elisa Barney; Bernardino, Rodrigo Barros; Jesus, Darlisson Marinho de

doi:10.1109/icdar.2019.00248

Cited by 8 publications

(1 citation statement)

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“…Since there is no single binarization method that would be perfect for all applications for document images, some initial attempts at the combination of widely known approaches have been made [6], although verified for a relatively small number of test images from earlier DIBCO datasets. Another interesting recent idea is the development of some methods, which should be balanced between the processing time and obtained accuracy, presented during the ICDAR 2019 Time-Quality Document Binarization Competition [7]. Some approaches presented during this competition were also based on the combination of multiple methods, e.g., based on supervised machine learning, including texture features, with the use of the XGBoost classifier and additional morphological post-processing, as well as, e.g., a combination of the Niblack [8] and Wolf [9] methods.…”

Section: Introductionmentioning

confidence: 99%

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e.g., based on the analysis of natural images or assuming the presence of some quality degradations, such as in historical document images.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: Introductionmentioning

confidence: 99%