Deductive method for recognition of on-line handwritten Persian/Arabic characters

Harouni, Majid; Mohamad, Dzulkifli; Rasouli, Abdolreza

doi:10.1109/iccae.2010.5451869

Cited by 19 publications

(12 citation statements)

References 9 publications

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“…The features have been implemented by [4], [5], [6], [7] and [8], and they have complementary distributions.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

“…Second, this feature is figured by Equation (3) for measuring the direction of the straight line from the start point and end point on each token, in [4], [6], [8] and [9].…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

“…The third feature is the orientation of each token (clockwise and counter clockwise), utilized in [4], [6], [7], [8], [10], [11] and [12]. Finally, this feature (see Equation (4)) is the ratio of length of each token in terms of the total length of stroke.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Finding Critical Points of Handwritten Persian/Arabic Character

Harouni¹,

Mohamad²,

Rahim³

et al. 2012

IJMLC

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Abstract-On-line handwritten character recognition system is a special line of research in image processing and pattern recognition field, it can also considered a special process of in academic researches and production fields in the past decade. The general steps in a recognition system are pre-processing, segmentation, feature extraction and classifications. The techniques of pre-processing stage play an excessive role in the system and directly affect the system performance. The focus of this article is on doing pre-processing stage and providing a most desirable data set form the raw data using in feature extraction and then to increase the rate of recognition system. For this reason, the novel algorithm is presented for finding a critical points set of hand-drawn letters. These critical points help extracting the correct structural and statistical features of on-line character handwriting in any given style writing.Index Terms-Persian and Arabic script, critical points, on-line character recognition, pre-processing.

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“…The features have been implemented by [4], [5], [6], [7] and [8], and they have complementary distributions.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

“…Second, this feature is figured by Equation (3) for measuring the direction of the straight line from the start point and end point on each token, in [4], [6], [8] and [9].…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Finding Critical Points of Handwritten Persian/Arabic Character

Harouni¹,

Mohamad²,

Rahim³

et al. 2012

IJMLC

Self Cite

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“…According to these features and some other like different position of diacritic marks on letters with common body, recognition of Persian and Arabic cursive words are difficult. In recent years, researchers tried to reduce this complexity [4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…So segmentation method has important effect on whole word recognition. Recently, different efforts made to propose segmentation method in online Persian and Arabic cursive word recognition [4][5][6][7][8][9][10][11]13].…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Segmentation of On-Line Persian Cursive Words

Golestani¹,

Khademi²,

Moeini³

2014

IJCATR

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Segmentation approaches, as processes that divide word into smaller parts which contain one letter at most, have important effect on cursive word recognition. While online cursive word recognition became applied technology in Latin and Chinese languages, complex structural features in Arabic-based script made it an important field of study in Persian and Arabic languages. In this paper, by introducing of Standard Persian Handwriting, we proposed a novel approach to segmentation online Persian cursive script based on width of letter's body in Persian language. Results are shown 99.86% accuracy in detection of expected segmentation points, while recognized extra points reduced 93.73% compared to our previous methods.

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Authentication through gender classification from iris images using support vector machine

Khan

Doosti²,

Karimi

et al. 2021

Microscopy Res & Technique

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Soft biometric information, such as gender, iris, and voice, can be helpful in various applications, such as security, authentication, and validation. Iris is secure biometrics with low forgery and error rates due to its highly certain features are being used in the last few decades. Iris recognition could be used both independently and in part for secure recognition and authentication systems. Existing iris‐based gender classification techniques have low accuracy rates as well as high computational complexity. Accordingly, this paper presents an authentication approach through gender classification from iris images using support vector machine (SVM) that has an excellent response to sustained changes using the Zernike, Legendre invariant moments, and Gradient‐oriented histogram. In this study, invariant moments are used as feature extraction from iris images. After extracting these descriptors' attributes, the attributes are categorized through keycode fusion. SVM is employed for gender classification using a fused feature vector. The proposed approach is evaluated on the CVBL data set and results are compared in state of the art based on local binary patterns and Gabor filters. The proposed approach came out with 98% gender classification rate with low computational complexity that could be used as an authentication measure.

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Deductive method for recognition of on-line handwritten Persian/Arabic characters

Cited by 19 publications

References 9 publications

Finding Critical Points of Handwritten Persian/Arabic Character

Finding Critical Points of Handwritten Persian/Arabic Character

A New Approach to Segmentation of On-Line Persian Cursive Words

Authentication through gender classification from iris images using support vector machine

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