Enhancement and cleaning of handwritten data by using neural networks and threshold technical

Varghahan, Behrooz Zali; Amirani, Mehdi Chehel; Mihandoost, Sara

doi:10.1109/icaict.2011.6111026

Cited by 4 publications

(7 citation statements)

References 7 publications

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“…1(c) and it will cause reducing both perceiving the handwriting on the image and the overall quality of document image. Existing local-based methods are non-learning-based methods and they are applied to the image without decision making [4,5,6,9]. By enhancing contrast of an image without learning-based method, the contrast of handwriting can be overenhanced which will decrease the quality of handwriting or empty area of the document will be enhanced which is not necessary.…”

Section: Learning-based Windowingmentioning

confidence: 99%

“…However, these methods mostly use filter techniques in window to reduce the noise by considering local parameters. For instance, Varghahan et al [4] proposed a window approach which uses median filter within the window to smooth pixels in the center of the window relative to the rest of pixels. Another window-based contrast enhancement technique was proposed in [6] and it uses a small window that slides through every image pixel sequentially.…”

Section: Learning-based Windowingmentioning

confidence: 99%

“…Latter approach is easier and faster than the former one to digitize the documents but it may bring undesired artifacts on the document images. On the other hand, digitized documents are widely used in the areas of image processing and pattern recognition to resolve the text image problems such as character detection and recognition [1,2], text binarization and reduction of the noise [3,4]. Another important problem is to enhance the quality of handwriting on the images which will affect the accuracy of the other text image problems.…”

Section: Introductionmentioning

confidence: 99%

“…There are local-based and global-based methods currently employed to enhance the visibility of text on the old document images [3,4,5,6,7]. Global enhancement methods are based on the general enhancement of all pixels on the images and enhancement of all image pixels improves text quality.…”

Section: Introductionmentioning

confidence: 99%

“…In [4], threshold approach combined with artificial neural network (ANN) is proposed for removing the noise and enhancing the handwriting on the scanned image for text recognition. The method uses several preprocessing methods such as noise reduction by using median filter, threshold technique and normalization.…”

Section: Introductionmentioning

confidence: 99%

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Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering

Kusetoğulları

Grahn

Lavesson

2016

2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

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Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering. In: 2016 IEEE International Symposium on Signal Processing and InformationTechnology, ISSPIT 2016, 7886054 (pp. 305-310 ABSTRACTIn this paper, a new approach is proposed to enhance the handwriting image by using learning-based windowing contrast enhancement and Gaussian Mixture Model (GMM). A fixed size window moves over the handwriting image and two quantitative methods which are discrete entropy (DE) and edge-based contrast measure (EBCM) are used to estimate the quality of each patch. The obtained results are used in the unsupervised learning method by using k-means clustering to assign the quality of handwriting as bad (if it is low contrast) or good (if it is high contrast). After that, if the corresponding patch is estimated as low contrast, a contrast enhancement method is applied to the window to enhance the handwriting. GMM is used as a final step to smoothly exchange information between original and enhanced images to discard the artifacts to represent the final image. The proposed method has been compared with the other contrast enhancement methods for different datasets which are Swedish historical documents, DIBCO2010, DIBCO2012 and DIBCO2013. Results illustrate that proposed method performs well to enhance the handwriting comparing to the existing contrast enhancement methods.Index Terms-Handwriting image enhancement, contrast enhancement, learning-based windowing, gaussian mixture modeling, k-means clustering.

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Section: Learning-based Windowingmentioning

confidence: 99%

Section: Learning-based Windowingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering

Kusetoğulları

Grahn

Lavesson

2016

2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

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Unsupervised Text Binarization in Handwritten Historical Documents Using k-Means Clustering

Kusetoğulları

2017

Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016

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In this paper, we propose a novel technique for unsupervised text binarization in handwritten historical documents using k-means clustering. In the text binarization problem, there are many challenges such as noise, faint characters and bleed-through and it is necessary to overcome these tasks to increase the correct detection rate. To overcome these problems, preprocessing strategy is first used to enhance the contrast to improve faint characters and Gaussian Mixture Model (GMM) is used to ignore the noise and other artifacts in the handwritten historical documents. After that, the enhanced image is normalized which will be used in the postprocessing part of the proposed method. The handwritten binarization image is achieved by partitioning the normalized pixel values of the handwritten image into two clusters using k-means clustering with k = 2 and then assigning each normalized pixel to the one of the two clusters by using the minimum Euclidean distance between the normalized pixels intensity and mean normalized pixel value of the clusters. Experimental results verify the effectiveness of the proposed approach.

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An Analytic Review on Image Enhancement Techniques Based on Soft Computing Approach

Kaur

Bhardwaj

Singh

2017

Advances in Intelligent Systems and Computing

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Enhancement and cleaning of handwritten data by using neural networks and threshold technical

Cited by 4 publications

References 7 publications

Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering

Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering

Unsupervised Text Binarization in Handwritten Historical Documents Using k-Means Clustering

An Analytic Review on Image Enhancement Techniques Based on Soft Computing Approach

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