OCR based feature extraction and template matching algorithms for Qatari number plate

Farhat, Ali; Al-Zawqari, Ali; Al-Qahtani, Abdulhadi; Hommos, Omar; Bensaali, Fayçal; Amira, Abbes; Zhai, Xiaojun

doi:10.1109/iccsii.2016.7462419

Cited by 16 publications

(7 citation statements)

References 2 publications

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“…Hamming distance [64], Euclidean distance [63,85], Cosines and scalar product [86], and Normalized Cross-Correlation (NCC) [87] were used to find the similarity between templates in many Arabic OCR approaches. Farhat et al [88] segmented the image of the Qatari Plate into characters, then each character image is divided into four zones, and finally, template matching is used to recognize the character label. Nosseir et al [44] extracted SURF features from the Egyptian ID Cards, then template matching is used to classify the characters.…”

Section: Template Matchingmentioning

confidence: 99%

Arabic Optical Character Recognition: A Review

Alghyaline¹

2023

Computer Modeling in Engineering &Amp; Sciences

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This study aims to review the latest contributions in Arabic Optical Character Recognition (OCR) during the last decade, which helps interested researchers know the existing techniques and extend or adapt them accordingly. The study describes the characteristics of the Arabic language, different types of OCR systems, different stages of the Arabic OCR system, the researcher's contributions in each step, and the evaluation metrics for OCR. The study reviews the existing datasets for the Arabic OCR and their characteristics. Additionally, this study implemented some preprocessing and segmentation stages of Arabic OCR. The study compares the performance of the existing methods in terms of recognition accuracy. In addition to researchers' OCR methods, commercial and open-source systems are used in the comparison. The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters. Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment, and few approaches were tested on pagelevel scripts. Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low, under 75% for printed text, and further improvement is needed. Moreover, most of the current approaches are offline OCR systems, and there is no remarkable contribution to online OCR systems.

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Section: Template Matchingmentioning

confidence: 99%

Arabic Optical Character Recognition: A Review

Alghyaline¹

2023

Computer Modeling in Engineering &Amp; Sciences

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“…The algorithm [12] Discussed extract the lines and curves which the alphabet is made is using the feature recognition based OCR. In [13] four different OCR techniques namely vectors crossing, Zoning, combination of vector crossing and Zoning and Template matching are proposed. The results obtained from these four methods are compared and it is shown that template matching technique yields better accuracy of 99.5% with an average time of 1.95 m sec per each character.…”

Section: Literature Surveymentioning

confidence: 99%

A novel priority based document image encryption with mixed chaotic systems using machine learning approach

Ravanna

Keshavamurthy²

2019

FACTA U EE

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Document images containing different types of information are required to be encrypted with different levels of security. In this paper, the image classification is carried out based on the feature extraction, for color images. The K-Nearest Neighbor (K-NN) method of image classification technique is used for classifying the query Document with trained set of features obtained from the Document database. Optical Character Recognition (OCR) technique is used to check for the presence as well as location of text/numerals in the Documents and to identify the Document type. Priority level is assigned in accordance with the Document type. Document images with different priorities are encrypted with different multi-dimensional chaotic maps. The Documents with different priority levels are diffused with different techniques. Document with highest priority are encrypted with highest level of security but Documents with lower priority levels are encrypted with lesser security levels. The proposed work was experimented for different document types with more number of image features for a large trained database. The results reveals a high speed of encryption for a set of document pages with priorities is more effective in comparison with a uniform method of encryption for all document types. The National Institute of Standards and Technology (NIST) statistical tests are also conducted to check for the randomness of the sequence and achieved good randomness. The proposed work also ensures security against the various statistical and differential attacks.

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“…Then, by formulating the assignment problem as a binary quadratic problem, which allows considering pairwise relations as well as local properties.In this paper, for character recognition following steps are followed-1) Find out Character 2) Choose the Character 3) For using the first template size of image is rescale 4) Comparison with Template 5) Record the maximum match as a indentified character. Ali Farhat et al [4] proposed four algorithms applied to Qatari number plates. The four algorithms are based on feature extraction (vector crossing, zoning, combined zoning, and vector crossing) and template matching techniques.…”

Section: Literature Survey a Suman Avdhesh Yadav Proposed A Systmentioning

confidence: 99%

Review of methods for Handwritten Character Identification using Optical Character Recognition (OCR)

Gurav¹

2019

IJRASET

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It is very hard to recognize handwritten character using optical character recognition. Also it is very difficult to convert handwritten image into text format. Optical character recognition has become one of the most successful applications of technology in the field of pattern recognition and artificial intelligence. OCR is a widespread technology to recognize text inside images, such as scanned documents and photos. This paper gives the survey of different techniques used by the researcher for dynamic pattern and character recognition.

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OCR based feature extraction and template matching algorithms for Qatari number plate

Cited by 16 publications

References 2 publications

Arabic Optical Character Recognition: A Review

Arabic Optical Character Recognition: A Review

A novel priority based document image encryption with mixed chaotic systems using machine learning approach

Review of methods for Handwritten Character Identification using Optical Character Recognition (OCR)

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