A new sharpness based approach for character segmentation in License plate images

Khare, Vijeta; Shivakumara, Palaiahnakote; Raveendran, P.; Meng, Liang Kim; Woon, Hon Hock

doi:10.1109/acpr.2015.7486562

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…This gives two stroke pixels, which are named as a stroke width pair. For each pair, the proposed method checks the direction given by the gradient vector flow (GVF), which is an improved version of the gradient direction and attracts towards edges, where there is a high force as defined in (1) [23]. If a stroke width pair has opposite directions to each other, the pair is said to be a satisfied GVF opposite arrow symmetry.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…If a stroke width pair has opposite directions to each other, the pair is said to be a satisfied GVF opposite arrow symmetry. Motivated by the observation in [23] where it is mentioned that pixels that represent texts satisfy the symmetry in Canny and Sobel edge images of the input image [22, 23], the symmetry is checked for the pixels in both Canny and Sobel edge images. The common pixels which satisfy the symmetry are considered as L‐CSPs.…”

Section: Proposed Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Symmetry features for license plate classification

Raghunandan¹,

Shivakumara

Padmanabhan

et al. 2018

CAAI Transactions on Intelligence Technology

Self Cite

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Achieving high recognition rate for license plate images is challenging due to multi-type images. We present new symmetry features based on stroke width for classifying each input license image as private, taxi, cursive text, when they expand the symbols by writing and non-text such that an appropriate optical character recognition (OCR) can be chosen for enhancing recognition performance. The proposed method explores gradient vector flow (GVF) for defining symmetry features, namely, GVF opposite direction, stroke width distance, and stroke pixel direction. Stroke pixels in Canny and Sobel which satisfy the above symmetry features are called local candidate stroke pixels. Common stroke pixels of the local candidate stroke pixels are considered as the global candidate stroke pixels. Spatial distribution of stroke pixels in local and global symmetry are explored by generating a weighted proximity matrix to extract statistical features, namely, mean, standard deviation, median and standard deviation with respect the median. The feature matrix is finally fed to an support vector machine (SVM) classifier for classification. Experimental results on large datasets for classification show that the proposed method outperforms the existing methods. The usefulness and effectiveness of the proposed classification is demonstrated by conducting recognition experiments before and after classification.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

Symmetry features for license plate classification

Raghunandan¹,

Shivakumara

Padmanabhan

et al. 2018

CAAI Transactions on Intelligence Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Using k-means clustering to discover linked pixel areas and combining appropriate pixels into components to efficiently extract each character, a method known as connected components labelling analysis (CCLA) was described in [8] for segmenting license plate (LP) characters. In [9], created a unique sharpness-based methodology for segmenting the characters in LP images. In the segmentation procedure, the model faced a gradient vector and a lack of accuracy.…”

Section: A Character Segmentationmentioning

confidence: 99%

Character Level Segmentation and Recognition using CNN Followed Random Forest Classifier for NPR System

Naidu¹,

Thiruvengatanadhan²,

Narayana³

et al. 2022

IJACSA

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The number plate recognition system must be able to quickly and accurately identify the plate in both low and noisy lighting conditions, as well as within the specified time limit. This study proposes automated authentication, which would minimize security and individual workload while eliminating the requirement for human credential verification. The four processes that follow the acquisition of an image are preprocessing, number plate localization, character segmentation, and character identification. A human error during the affirmation and the enrolling process is a distinct possibility since this is a manual approach. Personnel at the selected location may find it difficult and time-consuming to register and compose information manually. Due to the printed edition design, it is impossible to communicate the information. Character segmentation breaks down the number plate region into individual characters, and character recognition detects the optical characters. Our approach was tested using genuine license plate images under various environmental circumstances and achieved overall recognition accuracy of 91.54% with a single license plate in an average duration of 2.63 seconds.

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“…Also, few approaches were projected for character segmentation present in LP images. Khare et al [11] developed a novel sharpnessrelied model to segment the characters of LP images. The model encountered gradient vector and accurateness for segmenting operation.…”

Section: B Character Segmentationmentioning

confidence: 99%

Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

et al. 2020

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Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non-uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN) based recognition called OKM-CNN model. The proposed OKM-CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM-CNN model over other compared methods in a considerable way.

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A new sharpness based approach for character segmentation in License plate images

Cited by 8 publications

References 6 publications

Symmetry features for license plate classification

Symmetry features for license plate classification

Character Level Segmentation and Recognition using CNN Followed Random Forest Classifier for NPR System

Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

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