Document image segmentation using fuzzy classifier and the dual-tree DWT

Saeedi, Jamal; Safabakhsh, Reza; Mozaffari, Saeed

doi:10.1109/csicc.2009.5349611

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…e second step is the median filter which was applied by Mark [65] Complexity gamma intensity correction (GIC), which according to Saeedi et al is used for illumination variation [93]. In the fourth step, the iris image is exposed to a sequence of top hat and bottom hat filters, as suggested by Bai [94].…”

Section: Histogram and Filtering Methodmentioning

confidence: 99%

Iris Recognition Development Techniques: A Comprehensive Review

2021

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Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into seven phases, namely, the acquisition phase in which the iris images are acquired, the preprocessing phase in which the quality of the iris image is improved, the segmentation phase in which the iris region is separated from the background of the image, the normalization phase in which the segmented iris region is shaped into a rectangle, the feature extraction phase in which the features of the iris region are extracted, the feature selection phase in which the unique features of the iris are selected using feature selection techniques, and finally the classification phase in which the iris images are classified. This article also explains the two approaches of iris recognition which are the traditional approach and the deep learning approach. In addition, the researchers discuss the advantages and disadvantages of previous techniques as well as the limitations and benefits of both the traditional and deep learning approaches of iris recognition. This study can be considered as an initial step towards a large-scale study about iris recognition.

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Section: Histogram and Filtering Methodmentioning

confidence: 99%

Iris Recognition Development Techniques: A Comprehensive Review

2021

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“…NN is used to classify pixels as text and background. An efficient text extraction method using fuzzy classifier and dual-tree discrete wavelet transform is suggested by Saeedi 42 . Fuzzy classifier is applied to classify pixels as either text or non-text regions.…”

Section: Related Workmentioning

confidence: 99%

A New Approach to Extract Text from Images based on DWT and K-means Clustering

Ghai¹,

Gera²,

Jain³

2016

IJCIS

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Text present in image provides important information for automatic annotation, indexing and retrieval. Therefore, its extraction is a well known research area in computer vision. However, variations of text due to differences in orientation, alignment, font, size, low image contrast and complex background make the problem of text extraction extremely challenging. In this paper, we propose a texture-based text extraction method using DWT with K-means clustering. First, the edges are detected from image by using DWT. Then, a small size overlapped sliding window is used to scan high frequency component sub-bands from which texture features of text and non-text regions are extracted. Based on these features, K-means clustering is employed to classify the image into text, simple background and complex background clusters. Finally, voting decision process and area based filtering are used to locate text regions exactly. Experimentation is carried out using public dataset ICDAR 2013 and our own dataset for English, Hindi and Punjabi text images for different number of clusters. The results show that the proposed method gives promising results with different languages in terms of detection rate (DR), precision rate (PR) and recall rate (RR).

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Document segmentation using textural features summarization and feedforward neural network

Oyedotun

Khashman

2016

Appl Intell

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Document image segmentation using fuzzy classifier and the dual-tree DWT

Cited by 3 publications

References 17 publications

Iris Recognition Development Techniques: A Comprehensive Review

Iris Recognition Development Techniques: A Comprehensive Review

A New Approach to Extract Text from Images based on DWT and K-means Clustering

Document segmentation using textural features summarization and feedforward neural network

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