Efficient multiscale Sauvola’s binarization

Lazzara, Guillaume; Géraud, Thierry

doi:10.1007/s10032-013-0209-0

Cited by 59 publications

(44 citation statements)

References 31 publications

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“…For instance, for Sauvola's binarization approach (see [26] for a multiscale version), the thresholds computed at pixel-level do not vary a lot for the same object (given than the window used to compute thresholds has to be large). Another examples are the MSER and ER approaches [7], where objects come from the image level sets.…”

Section: B Qualitative Interpretation Of Resultsmentioning

confidence: 99%

Morphology-based hierarchical representation with application to text segmentation in natural images

Huỳnh

Géraud³

2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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Abstract-Many text segmentation methods are elaborate and thus are not suitable to real-time implementation on mobile devices. Having an efficient and effective method, robust to noise, blur, or uneven illumination, is interesting due to the increasing number of mobile applications needing text extraction. We propose a hierarchical image representation, based on the morphological Laplace operator, which is used to give a robust text segmentation. This representation relies on several very sound theoretical tools; its computation eventually translates to a simple labeling algorithm, and for text segmentation and grouping, to an easy tree-based processing. We also show that this method can also be applied to document binarization, with the interesting feature of getting also reverse-video text.

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Section: B Qualitative Interpretation Of Resultsmentioning

confidence: 99%

Morphology-based hierarchical representation with application to text segmentation in natural images

Huỳnh

Géraud³

2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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“…• State of the art: Su et al [14], Sauvola MS (Lazzara and Geraud [12]), Howe [16], and Kliger and Tal [18]. Recent methods that constitute the state of the art for document image binarization.…”

Section: Comparative Assessmentmentioning

confidence: 99%

A selectional auto-encoder approach for document image binarization

Calvo-Zaragoza

Gallego

2019

Pattern Recognition

139

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Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of documents analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a global threshold. This approach has proven to outperform existing binarization strategies in a number of document types.

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“…However, detecting the size of a character usually requires image segmentation [23] and this is difficult for degraded documents. The window size value is a very important parameter [24], which has a strong influence on obtaining good binarization results. Moghaddam and Cheriet [1] proposed a method, which starts with a large window size and iteratively reduces it to a proper window size.…”

Section: B Background Estimationmentioning

confidence: 99%

Improved Degraded Document Image Binarization Using Median Filter for Background Estimation

Khitas¹,

Ziet²,

Bouguezel³

2018

ElAEE

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In this paper, we propose a new binarization method suitable for images having a variety of sizes and degradation levels. It is mainly based on the idea of estimating a document background surface by new smoothing approach through a median filter and compute-Sharp-Peak to replace iterative polynomial. The resulting document image is then segmented by a global threshold binarization. The simulation results confirm that the performance of the proposed method is generally competitive to that of the existing methods. For highly degraded document images, specifically the documents in the BICKLEY DIARY data-base, the performance of the former is substantially better than that of the latter. We show how this approach outperforms the existing and widely used binarization methods in terms of accuracy, F-measure, PSNR, NRM, MPM, DRD, recall and precision.

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Efficient multiscale Sauvola’s binarization

Cited by 59 publications

References 31 publications

Morphology-based hierarchical representation with application to text segmentation in natural images

Morphology-based hierarchical representation with application to text segmentation in natural images

A selectional auto-encoder approach for document image binarization

Improved Degraded Document Image Binarization Using Median Filter for Background Estimation

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