Feature Extraction in Six Blocks to Detect and Recognize English Numbers

Abbas, Heba Kh.; Mohamad, Haidar J.

doi:10.24996/ijs.2021.62.10.37

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…AdaBoost algorithm carries out Haar's features and classifier training simultaneously, and the meaning of AdaBoost explained by Ada is adaptive and Boost from Boosting. It is considered an iterative process and trains multi-classifiers by a training set to assemble a strong classifier from a weak classifier [19]. This algorithm is used to eliminate unnecessary data and used as a training set.…”

Section: Theoretical Conceptmentioning

confidence: 99%

Detection and Recognition of Car Plates in Parking Lots at Baghdad University

Abbas

Mohamad²

2023

Iraqi Journal of Science

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A simple and smart algorithm was presented to recognize car plates in parking at the College of Science for Women, University of Baghdad, Iraq. The study consists of recording video clips of all cars parked in the selected area. The studied camera heights were1m and 2m, and the video clips were 19 and 30. Images were extracted from the video clip to be used for training data for the cascade method. Haar classification was used to detect license plates after the training step. Viola-jones algorithm was applied to the output of Haar’s data for both camera heights (1m and 2m). The accuracy was calculated for all data with different weather conditions and local time recoding. The accuracy is 100% for all data in this study.

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Section: Theoretical Conceptmentioning

confidence: 99%

Detection and Recognition of Car Plates in Parking Lots at Baghdad University

Abbas

Mohamad²

2023

Iraqi Journal of Science

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“…The computation of unknown polynomial coefficients requires extensive numerical operations, often necessitating the use of a computational programming language. In this study, MATLAB, a specialized software application known for its problem-solving and graphical representation capabilities, was utilized [21]. MATLAB is commonly operated interactively, allowing the user to input expressions and receive immediate results.…”

Section: Code Development For Osm Data Improvementmentioning

confidence: 99%

Enhancing Spatial Accuracy of OpenStreetMap Data: A Geometric Approach

Al-Bakri

2023

MMEP

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OpenStreetMap (OSM), recognised for its current and readily accessible spatial database, frequently serves regions lacking precise data at the necessary granularity. Global collaboration among OSM contributors presents challenges to data quality and uniformity, exacerbated by the sheer volume of input and indistinct data annotation protocols. This study presents a methodological improvement in the spatial accuracy of OSM datasets centred over Baghdad, Iraq, utilising data derived from OSM services and satellite imagery. An analytical focus was placed on two geometric correction methods: a two-dimensional polynomial affine transformation and a two-dimensional polynomial conformal transformation. The former involves twelve coefficients for adjustment, while the latter encompasses six. Analysis within the selected region exposed variances in positional accuracy, with distinctions evident between Easting (E) and Northing (N) coordinates. Empirical results indicated that the conformal transformation method reduced the Root Mean Square Error (RMSE) by 4.434 meters in the amended OSM data. Contrastingly, the affine transformation method exhibited a further reduction in total RMSE by 4.053 meters. The deployment of these proposed techniques substantiates a marked enhancement in the geometric fidelity of OSM data. The refined datasets have significant applications, extending to the representation of roadmaps, the analysis of traffic flow, and the facilitation of urban planning initiatives.

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“…Detecting and distinguishing objects considered one of the basic tasks within the concept of computer vision [2]. It depends on identifying and defining these objects, whether these objects are in the form of images or video sequences [3].…”

Section: Introductionmentioning

confidence: 99%

Optical System to Recognize Car Plate Ownership

Abbas

Mohamad

2023

Mal. J. Fund. Appl. Sci.

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The process development of the image processing can solve the problem of detection and recognition of the license plate by taking pictures of the cars and then recognizing them. Most traffic applications rely on automatic vehicle plate detection in parking lots, border control, speed control, etc. In this study, a smart visual system was presented to identify car plates in the College of Science for Girls - University of Baghdad parking lot. The work included distinguishing the car plate and identifying cars, whether they belonged to the college or not. This process was based on the Cascade Classifier method based on the Viola-Jones algorithm, and a database for all car plate features was stored in a file using the proposed method. The recognized car was compared with the characteristics of the database using Oriented FAST and Rotated BRIEF then features were extracted using Histograms of Oriented Gradients. The license plate is recognized when matching features are employed using the matching feature’s function. The results of congruence and discrimination were excellent and very highly efficient. The luminous intensity dependence is considered, as the work is based on the red band of the car's image.

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Feature Extraction in Six Blocks to Detect and Recognize English Numbers

Cited by 3 publications

References 15 publications

Detection and Recognition of Car Plates in Parking Lots at Baghdad University

Detection and Recognition of Car Plates in Parking Lots at Baghdad University

Enhancing Spatial Accuracy of OpenStreetMap Data: A Geometric Approach

Optical System to Recognize Car Plate Ownership

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