A fuzzy expert system for recognition of handwritten arabic sub-words

Khedher, Mohammed Z.; Al-Talib, Ghaydaa

doi:10.1109/isspa.2007.4555355

Cited by 4 publications

(2 citation statements)

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“…Combining a fuzzy linguistic model and nonfuzzy one allows a simple qualitative representation of the feature knowledge for the Arabic characters. Other approaches [19] [20] were also reported in the literature.…”

Section: Related Workmentioning

confidence: 99%

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An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

Darwish

Elzoghaly

2020

IEEE Access

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In the recent few years, there was a concentrated search on Arabic Optical Character Recognition (OCR), especially the recognition of scanned, offline, machine-printed documents. However, Arabic OCR consequences are dissatisfying and are still a developed research area. Finding the best feature extraction techniques and selecting an appropriate classification algorithm lead to supreme recognition accuracy and low computational overhead. This paper presents a new Arabic OCR model by integrating both of Genetic Algorithm (GA) and the Fuzzy K-Nearest Neighbor classifier (F-KNN) in a unified framework to enhance the identification accuracy. GA is utilized as a feature selection algorithm that has better convergence and spread of solutions with candid variation preservation mechanism. The F-KNN algorithm is more appropriate to classify ambiguous or uncertain data objects in the sense that every object belongs to all classes with different degrees of membership. The suggested model semantically fuses bio-inspired based feature vectors with fuzzy KNN classifier to build accurate membership function for each class. Experimental results compared to other approaches revealed the effectiveness of the suggested model and demonstrated that the feature selection approach increased the identification accuracy process.

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Section: Related Workmentioning

confidence: 99%

“…The basis of this algorithm is to assign membership as a function of the vectors distance from its K-nearest neighbors and those neighbors' memberships in the possible classes [10] [19]. Let W = {x 1, x2, .…”

Section: F Classification Using Fuzzy K-nnmentioning

confidence: 99%

An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

Darwish

Elzoghaly

2020

IEEE Access

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Estimating the Effectiveness of CNN and Fuzzy Logic for Signature Verification

Priya.

Chaurasia

2023

2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)

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Fuzzified Deep Learning based Forgery Detection of Signatures in the Healthcare Mission Records

Priya,

Chaurasia,

Singh

et al. 2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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In an era subjected to digital solutions, handwritten signatures continue playing a crucial role in identity verification and document authentication. These signatures, a form of bio-metric verification, are unique to every individual, serving as a primitive method for confirming identity and ensuring security of an individual. Signatures, apart from being a means of personal authentication, are often considered a cornerstone in the validation of critical documents and processes, especially within the healthcare sector. In healthcare missions, particularly in the regions that are underdeveloped, hand-written records persist as the primary mode of documentation. The credibility of these handwritten documents hinges on the authenticity of the accompanying signatures, making signature verification a paramount safeguard for the integrity and security of medical information. Nonetheless, traditional offline methods of signature identification can be time-consuming and inefficient, particularly while dealing with a massive volume of documents. This arises the evident need for automated signature verification systems. Our research introduces an innovative signature verification system which synthesizes the strengths of fuzzy logic and CNN (Convolutional Neural Networks) to deliver precise and efficient signature verification. Leveraging the capabilities of Fuzzy Logic for feature representation and CNNs for discriminative learning, our proposed hybrid model offers a compelling solution. Through rigorous training, spanning a mere 28 epochs, our hybrid model exhibits remarkable performance by attaining a training accuracy of 91.29% and a test accuracy of 88.47%, underscoring its robust generalization capacity. In an era of evolving security requirements and the persistent relevance of handwritten signatures, our research links the disparity between tradition and modernity.

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A fuzzy expert system for recognition of handwritten arabic sub-words

Cited by 4 publications

References 1 publication

An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

Estimating the Effectiveness of CNN and Fuzzy Logic for Signature Verification

Fuzzified Deep Learning based Forgery Detection of Signatures in the Healthcare Mission Records

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