GGCS: A Greedy Graph-Based Character Segmentation System for Bangladeshi License Plate

Rahman, Rashik; Pias, Tanmoy Sarkar; Helaly, Tanjina

doi:10.1109/ismsit50672.2020.9254449

Cited by 4 publications

(2 citation statements)

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“…The second is the recognition dataset (cropped image). This processing of a single image is adapted from [6] , and the YOLOv4 methodologies were adapted from [ 7 , 8 ].…”

Section: Data Descriptionmentioning

confidence: 99%

BDSL 49: A comprehensive dataset of Bangla sign language

et al. 2023

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“…The second is the recognition dataset (cropped image). This processing of a single image is adapted from [6] , and the YOLOv4 methodologies were adapted from [ 7 , 8 ].…”

Section: Data Descriptionmentioning

confidence: 99%

BDSL 49: A comprehensive dataset of Bangla sign language

et al. 2023

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“…The accuracy of the algorithm is 99.75%. However, they filtered out non-digit and non-character blobs manually [29].…”

Section: Related Workmentioning

confidence: 99%

A Character Segmentation Method to Increase Character Recognition Accuracy for Turkish License Plates

Cavdaroglu¹,

Gokmen²

2021

MCS

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Automatic License Plate Recognition is a computer vision technology that provides a way to recognize the vehicle's license plates without direct human intervention. Developing Automatic License Plate Recognition methodologies is a widely studied topic among the computer vision community to increase the accuracy rates. Automatic License Plate Recognition systems include image acquisition and character segmentation phases. Although there are many studies, the research in character segmentation and improving recognition accuracy remains limited. The lack of an international standard for license plates and the misinterpretation of ambiguous characters are challenging problems for Automatic License Plate Recognition systems. Several academic works have shown that the ambiguous character problem can be overcome by using a second model that contains only these characters. In this study, we propose a new methodology to reduce the character recognition errors of Automatic License Plate Recognition systems. One of the reasons for the low accuracy rates is the problem of ambiguous characters. In most studies using OCR, it was observed that a single model was used for alphanumeric characters during the recognition phase. Instead of using a single model, using separate models for letters and digits will improve the recognition process and increase accuracy. Therefore, we determined whether the characters are letters or numbers, and we expressed the license plates in the form of letters -digits. The method suggested for segmenting blobs worked with an accuracy of 96.12% on the test dataset. The method recommended for generating letter-digit expressions for the license plates worked with an accuracy of 99.28% on the test dataset. The proposed methodology can work only on Turkish license plates. In future studies, we will expand our method by using the license plate dataset of a different country.

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