Gabor Wavelet Recognition Approach for Off-Line Handwritten Arabic Using Explicit Segmentation

Elzobi, Moftah; Al-Hamadi, Ayoub; Aghbari, Zaher Al; Dings, Laslo; Anwar, Saeed

doi:10.1007/978-3-319-01622-1_29

Cited by 19 publications

(9 citation statements)

References 13 publications

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“…Pal et al in [4] has suggested the recognition model through the curvature feature values of each individual and 94.6% as accuracy over the datasets used for the system. Subsequently analysis of various strokes that may be of horizontal or vertical type that reported by Bhomik et al in [6]. On partitioning the dataset into proper training and testing ratio they have listed up 95.89%, 90.50% classification accuracy for both training and testing phase.…”

Section: Survey On Related Workmentioning

confidence: 98%

“…Here in the proposed work we have put emphasis on the statistical value rather than structural one. As we have various orientation and similar shape of the numeral images of different individuals, to have a proper feature value we have evaluate a 2d-Gabor wavelet transform [6] over all the data images and reported the desired feature vector. In addition to it we have also added a step called dimension reduction of obtained feature vector and termed as the key feature values.…”

Section: Introductionmentioning

confidence: 99%

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A Gabor Wavelet Based Approach for Off-Line Recognition of ODIA Handwritten Numerals

Sethy¹,

Patra²,

Nayak³

et al. 2018

IJET

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Optical Character Recognition is one of the most interesting and highly motivated areas of research, which has been very much ap-preciated in different aspect to the area of digitations world. Here in this paper we have suggested a probabilistic approach for develop-ing recognition system for handwritten Odia numerals. To report a good level of recognition of Odia scripts is quite challenging with respect to other Indian scripts .All the procedure are sequentially enclosed to develop an recognition model and report a successful recognition accuracy. Here we have performed the analysis over to standard handwritten numeral database named as IITBBS Odia Numeral Database, which is collected from IIT Bhubaneswar. In the suggestive recognition system we have adopted a 2D-Gabor wavelet transformation approach for selection of feature vector. Apart from it we have also noted down the dimensional reduction to the obtained feature vector by sustaining to PCA. In order to predict high recognition rate we have followed up by RBF Neural Network classifier. In addition to it we have also evaluate different version of RBF like Gaussian and Polynomial. Performing over 400 samples each of 10 categories (400*10) number of Odia numeral images, we have maintained a well-defined training and testing ratio in the clas-sifier and achieved 98.02%, 96.8%.recognition rate for the reported classifiers.

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Section: Survey On Related Workmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A Gabor Wavelet Based Approach for Off-Line Recognition of ODIA Handwritten Numerals

Sethy¹,

Patra²,

Nayak³

et al. 2018

IJET

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“…Gabor filter features achieved recognizable performance in handwriting recognition [16] [17] [18] [24] [25], machine-printed text recognition [26], writer identification [27], script identification [28] and handwritten vs. machine-printed text identification [29]. Gabor filters were utilized in implementing early feature learning frameworks inspired by the Hubel and Wiesel model, e.g., the Neocognitron [30], Cresceptron [31] and HMAX [32].…”

Section: Gabor Filters Response Featuresmentioning

confidence: 99%

Recognition of Arabic Handwritten Words using Gabor-based Bag-of-Features Framework

al.¹

2018

IJCDS

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Holistic recognition of isolated words is an essential task in several daily life applications, e.g., bank check processing and postal address reading. In this work we present a system for the automatic recognition of Arabic handwritten words based on statistical features extracted by Bag-of-Features framework that exploits the discriminative power of Gabor features. A handwritten text image is filtered by a set of Gabor filters of different scales and orientations for extracting texture-based local features. The response of the Gabor filters are organized into two layouts, viz. the Statistical Gabor Features and Gabor Descriptors, and fed to the Bag-of-Features in order to produce statistical representations for the handwritten text. The produced features are utilized in a holistic handwritten word recognition system that is applied on handwritten Arabic checks legal amounts public dataset. The effective parameters of the two layouts as well as the Bag-of-Features framework are experimentally evaluated and the optimal values are used in reporting the final recognition accuracies. The best average recognition accuracy achieved by the produced features is 86.44% which is promising in such challenge dataset of large number of classes.

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“…Elzobi et al [18] extracted the mean and standard deviation of the Gabor wavelet transformed images.…”

Section: Sahlol and Suenmentioning

confidence: 99%

An artificial immune system for offline isolated handwritten arabic character recognition

Boufenar

Batouche²,

Schoenauer

2016

Evolving Systems

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Character recognition plays an important role in the modern world. In recent years, character recognition systems for different languages has gain importance. The recognition of Arabic writing is still an important challenge due to its cursive nature and great topological variability. The Artificial Immune System is a supervised learning technique that embodies the concepts of natural immunity to cope with complex classification problems. The objective of this research is to investigate the applicability of an Artificial Immune System in Offline Isolated Handwritten Arabic Characters. The developed system is composed of three main modules: preprocessing, feature extraction and recognition. The system was trained and tested with tenfold cross-validation technique on an original realistic database that we built from the well-known IFN/ENIT benchmark. Parameter tuning was performed with a grid-search algorithm with leave-one-out cross-validation. The obtained results of the proposed system are promising with a classification rate of 93.25% and often outperform most well-known classifiers from Scikit Learn Library.

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Gabor Wavelet Recognition Approach for Off-Line Handwritten Arabic Using Explicit Segmentation

Cited by 19 publications

References 13 publications

A Gabor Wavelet Based Approach for Off-Line Recognition of ODIA Handwritten Numerals

A Gabor Wavelet Based Approach for Off-Line Recognition of ODIA Handwritten Numerals

Recognition of Arabic Handwritten Words using Gabor-based Bag-of-Features Framework

An artificial immune system for offline isolated handwritten arabic character recognition

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