Arabic character recognition using a Haar cascade classifier approach (HCC)

AbdelRaouf, Ashraf; Higgins, Colin; Pridmore, Tony; Khalil, Mahmoud

doi:10.1007/s10044-015-0466-2

Cited by 10 publications

(2 citation statements)

References 43 publications

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“…As for the other hyperparameters related to the degenerate trees of weak classifiers, we fixed the weight trim rate to 0.95, maximal weak tree depth to 1, and maximal weak trees per stage to 100. As suggested in [51], a Gentle Adaboost, which typically enhances the generalization performance, is…”

Section: B Tail Number Recognition 1) Tail Number Detectionmentioning

confidence: 99%

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

2022

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Aircraft identification in airport operations is critical to various applications, including airport planning and environmental studies. Previous research and commercially available systems heavily rely on recognizing aircraft tail numbers using text recognition. However, this approach alone does not provide accurate results in situations when the tail number visibility is reduced or obstructed. Furthermore, general aviation aircraft are harder to identify because they are small in size, and their tail numbers include substantial variations in fonts, sizes, and orientations. To tackle these issues, we propose a two-step computer vision-based aircraft identification method, first identifying the aircraft type and then recognizing the tail number in a probabilistic multi-frame-based (MFB) framework. In the first step, a convolutional neural network (CNN)-based aircraft classifier is customized to decrease the search space in the registration database. In the second step, the identification process is finalized by integrating the text recognition results into the designed probabilistic MFB framework. The proposed method achieves approximately 90% identification accuracy when tested on video data collected from three general aviation airports. This is a significant improvement compared to text recognition alone, which recognizes 67% of the individual tail number characters.INDEX TERMS Intelligent transportation systems, computer vision, airports, aircraft.

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Section: B Tail Number Recognition 1) Tail Number Detectionmentioning

confidence: 99%

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

2022

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“…Another level of combination was also considered, that is, merging the best three rules within the proposed ones. An approach that is suitable for Arabic glyph recognition using the Haar Cascade classifier (HCC) approach was reported [10]. HCC ignores problematical steps in the preprocessing, recognition, and character segmentation stages.…”

Section: Introductionmentioning

confidence: 99%

Enhanced technique for Arabic handwriting recognition using deep belief network and a morphological algorithm for solving ligature segmentation

2018

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Arabic handwriting segmentation and recognition is an area of research that has not yet been fully understood. Dealing with Arabic ligature segmentation, where the Arabic characters are connected and unconstrained naturally, is one of the fundamental problems when dealing with the Arabic script. Arabic character‐recognition techniques consider ligatures as new classes in addition to the classes of the Arabic characters. This paper introduces an enhanced technique for Arabic handwriting recognition using the deep belief network (DBN) and a new morphological algorithm for ligature segmentation. There are two main stages for the implementation of this technique. The first stage involves an enhanced technique of the Sari segmentation algorithm, where a new ligature segmentation algorithm is developed. The second stage involves the Arabic character recognition using DBNs and support vector machines (SVMs). The two stages are tested on the IFN/ENIT and HACDB databases, and the results obtained proved the effectiveness of the proposed algorithm compared with other existing systems.

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Recognizing human behaviors from surveillance videos using the SSD algorithm

Pan

Zhao

2021

J Supercomput

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Arabic character recognition using a Haar cascade classifier approach (HCC)

Cited by 10 publications

References 43 publications

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

Enhanced technique for Arabic handwriting recognition using deep belief network and a morphological algorithm for solving ligature segmentation

Recognizing human behaviors from surveillance videos using the SSD algorithm

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