License plate recognition based on fractal and hidden Markov feature

Qing-tian, 耿庆田 Geng; Hong-wei, 赵宏伟 Zhao

doi:10.3788/ope.20132112.3198

Cited by 5 publications

(2 citation statements)

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“…Hessian matrix discriminant such as type (2).The value of the discriminant is the eigenvalues of the Hessian matrix. It classifies all points using symbols of determining structure and according to the discriminant value plus or minus identifying whether this point is the value of the pole.…”

Section: Localization Algorithmmentioning

confidence: 99%

Multiple Vehicle License Plate Location in Complex Background

Zhao

Li³

2018

International Journal of Advanced Network, Monitoring and Controls

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In order to expand the application range of the intelligent traffic management system, and to solve the problem that the license plate positioning accuracy is low in the changing of the scene. On the basis of the analysis of previous methods advantages and disadvantages, applying deep learning model orientation method is proposed. The image expressed as graph of graph theory .Based on the principle of minimum spanning tree preliminary separate target objects in image of vehicle. Combined with the color, dimension, texture and match the similarity to choose, search and merger area in the image, suspicious area of the license plate is obtained. Using visual word package to express rectangular profile after coarse positioning. Using support vector machine (SVM) to classify and identify rectangular area of license plate. Accurate positioning license plate location is positioned accurately. The method of accuracy is 96.4% for 135 pieces of test sample positioning, strong anti-jamming.

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Section: Localization Algorithmmentioning

confidence: 99%

Multiple Vehicle License Plate Location in Complex Background

Zhao

Li³

2018

International Journal of Advanced Network, Monitoring and Controls

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“…Most of the traditional character recognition methods are based on the similarity of template matching, edge information in the image, shape features, and statistical classification techniques. To address the feature extraction problem of steel-stamped characters, Geng et al [4] used a method based on fractal dimension and Hidden Markov features to binarize the characters and then used multiple classifiers to recognize the embossed characters of license plates. Zhang et al [5] proposed an embossed character segmentation method, which firstly screens the embossed character region, then performs morphological optimization, and combines the extracted embossed character features with a BP neural network to achieve embossed character recognition.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOV5 Angle Embossed Character Recognition by Multiscale Residual Attention with Selectable Clustering

Ying,

Fang,

Tang

et al. 2024

Electronics

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In the intelligentization process of power transmission towers, automated identification of stamped characters is crucial. Currently, manual methods are predominantly used, which are time-consuming, labor-intensive, and prone to errors. For small-sized characters that are incomplete, connected, and irregular in shape, existing OCR technologies also struggle to achieve satisfactory recognition results. Thus, an approach utilizing an improved deep neural network model to enhance the recognition performance of stamped characters is proposed. Based on the backbone network of YOLOv5, a multi-scale residual attention encoding mechanism is introduced during the upsampling process to enhance the weights of small and incomplete character targets. Additionally, a selectable clustering minimum iteration center module is introduced to optimize the selection of clustering centers and integrate multi-scale information, thereby reducing random errors. Experimental verification shows that the improved model significantly reduces the instability caused by random selection of clustering centers during the clustering process, accelerates the convergence of small target recognition, achieves a recognition accuracy of 97.6% and a detection speed of 43 milliseconds on the task of stamped character recognition, and significantly outperforms existing Fast-CNN, YOLOv5, and YOLOv6 models in terms of performance, effectively enhancing the precision and efficiency of automatic identification.

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Application of Improved HMM in Rolling Bearing Fault Diagnosis

Yang

Sun

2023

2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI)

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License plate recognition based on fractal and hidden Markov feature

Cited by 5 publications

References 0 publications

Multiple Vehicle License Plate Location in Complex Background

Multiple Vehicle License Plate Location in Complex Background

Improved YOLOV5 Angle Embossed Character Recognition by Multiscale Residual Attention with Selectable Clustering

Application of Improved HMM in Rolling Bearing Fault Diagnosis

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