Automatic license plate recognition using mobile device

Ngoc, Hung; Vo, Minh-Thanh; Vuong, Bao Quoc; Pham, Hoang Anh; Nguyen, An Hoang; Luong, Huy Quoc

doi:10.1109/atc.2016.7764786

Cited by 15 publications

(10 citation statements)

References 13 publications

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“…The plate region was extracted using a bounding box and a template matching method relying on a correlation for recognition of each character in the number plate. Do et al [2] developed an Android program that processes the image acquired by the built-in camera of a mobile device to capture the license plate number and save it into a database for subsequent applications. They imported open-source Open CV libraries into the project for selected image processing steps to reduce the programming time.…”

Section: Recent Workmentioning

confidence: 99%

“…where the interval of the angles is ∈ [ 1,2 ]. In this research, the end values of the interval were empirically chosen as -10 and 10, and the function S is given by the equation (14) [9], [10].…”

Section: Fuzzified Gabor Filtermentioning

confidence: 99%

“…In the low enforcement applications, the controlled illumination, capturing distance and angle, and image quality (by using a special REG cameras) ensure robust performance. With the commercial image qualities, as used in this work, the limitations have to be imposed at least on the capturing settings (distance and angle), with similar examples in [1], [2], [5], [11], [18], [25], [28], [36].The fair comparisons of the license plate algorithms developed under the similar assumptions and for the predefined settings would be possible only over the same image database. However, such an exhaustive comparison is beyond a scope of this paper.…”

Section: Validation Of the Algorithmsmentioning

confidence: 99%

“…The recognition of license plate's characters falls under the more general and well explored problem of characters' segmentation and recognition. There are numerous algorithms with an excellent performance that can be easily adapted to any license plate recognition system [2]. However, the extraction of a license plate from an image still remains a highly researched issue within object recognition, with many papers still being published on the topic [1], [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Tadić¹,

Király²,

Ódry³

et al. 2020

ACTA POLYTECH HUNG

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This paper presents a comparative study of improvements to the algorithms for license plate extraction from images captured using conventional, modest-quality cameras. It compares the results and efficacy of two similar algorithms which primarily differ in the raw image pre-processing stage of the initial image. One algorithm uses the Gabor filter bank with distinctly crisp parameters; the other relies on the fuzzified Gabor filter bank with fuzzified parameters to facilitate their adjustment. Results indicate that the fuzzy reasoning introduced for Gabor filter parameter adjustment improves the detection of the components of interest in complex images and adds minimal deviation compared to the Gabor filter bank with distinctly crisp parameters.

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Section: Recent Workmentioning

confidence: 99%

Section: Fuzzified Gabor Filtermentioning

confidence: 99%

Section: Validation Of the Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Tadić¹,

Király²,

Ódry³

et al. 2020

ACTA POLYTECH HUNG

View full text Add to dashboard Cite

show abstract

“…However, the small computers now available are powerful, use very little power and the software needed is simple and versatile. The use of these little devices is economical and they can easily be used for such applications as goods inventory [15,16].…”

Section: Mechanical Design Of the Grippermentioning

confidence: 99%

A Remote Controlled Robotic Arm That Reads Barcodes and Handles Products

Chen

2018

Inventions

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Abstract:In this study, a 6-axis robotic arm, which was controlled by an embedded Raspberry Pi with onboard WiFi, was developed and fabricated. A mobile application (APP), designed for the purpose, was used to operate and monitor a robotic arm by means of a WiFi connection. A computer vision was used to read common one-dimensional barcode (EAN code) for the handling and identification of products such as milk tea drinks, sodas and biscuits. The gripper on the end of the arm could sense the clamping force and allowed real-time control of the amount of force used to hold and handle the products. The packages were all made of different material and this control allowed them to be handled without danger of damage or deformation. The maximum handling torque used was 1.08 Nm and the mechanical design allowed the force of the gripper to be uniformly applied to the sensor to ensure accurate measurement of the force.

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Mobile‐aided screening system for proliferative diabetic retinopathy

Boukadida

Elloumi

Akil

et al. 2021

Int J Imaging Syst Tech

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NeoVascularization (NV) occurs in the Proliferative Diabetic Retinopathy (PDR) stage, where the development progress of new vessels presents a high risk for severe vision loss and blindness. Therefore, early NV detection is primordial to preserve patient's vision. Several automated methods have been proposed to detect the NV on retinograph-captured fundus images. However, their employment is constrained by the reduced ophthalmologist-per-person ratio and the expensive equipment for image capturing. This paper presents a novel method for NV detection in smartphone-captured fundus images. The implementation of the method on a smartphone having an optical lens for fundus capturing leads to a Mobiles-Aided-Screening system of PDR (MAS-PDR). The challenge is to ensure accurate and robust detection even with moderate quality of fundus image, on reduced execution time.Within this objective, we identify the major criteria of neovascularized vessels which are tortuosity, width, bifurcation, and density. Our main contribution consists in proposing a sharp feature to reflect each criterion on reduced computational complexity processing. Therefore, the features are provided to a random forest classifier to deduce the PDR stage.A dataset raised from publicly databases is used on a 10-cross validation process where 98.69% accuracy, 97.73% sensitivity, and 99.12% specificity are achieved. To evaluate the robustness, the same experimentation is repeated after applying motion blur filters to the fundus image dataset, where 98.91% accuracy, 96.75% sensitivity, and 100% specificity are deduced. Moreover, NV screening is performed under 3 s when executed in smartphone devices demonstrating the appropriateness of our method to MAS-PDR.

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Automatic license plate recognition using mobile device

Cited by 15 publications

References 13 publications

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

A Remote Controlled Robotic Arm That Reads Barcodes and Handles Products

Mobile‐aided screening system for proliferative diabetic retinopathy

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