Template Matching for Automatic Number Plate Recognition System with Optical Character Recognition

Vaishnav, Arun; Mandot, Manju

doi:10.1007/978-981-13-7166-0_69

Cited by 12 publications

(4 citation statements)

References 9 publications

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“…To use template matching, a template of the target object must first be created. Cropping the image of the object will enable you to perform this [61], [62]. Once you've generated it, you can use the template to scan the search image.…”

Section: E Vehicle Detectionmentioning

confidence: 99%

Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach

Yusuf,

Hanzla,

Rahman

et al. 2024

IEEE Access

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Systems must be capable of detecting and tracking autonomous vehicles for intelligent management and control of transportation. Even though several methods are used to create intelligent systems for traffic monitoring, this article explains how to detect and track vehicles using pixel labeling and particle filter algorithms. We suggested a novel technique that segments the image using image segmentation to retrieve the foreground objects. We have divided our proposed model into the following steps: at first, geo-referencing is used to find the exact location; secondly, the images are denoised by using preprocessing; image segmentation is used to separate the background from the foreground; multiple objection detection is performed using the random forest to classify different objects; vehicles are detected through a method called template matching; after this, the vehicles are counted using histogram of oriented gradients (HOG); and after counting, the tracking of vehicles is obtained using particle filter; lastly, the trajectories are predicted by comparing the rectangular centroid of each car against the frame number and using it as a time stamp reference, the last match that the tracking algorithm obtained for each vehicle was recorded and used to estimate the trajectories. Our model outperforms current traffic monitoring approaches in terms of detection and tracking accuracy by 0.87 and 0.92, respectively using the Aerial Car dataset and 0.84 and 0.88, respectively, using the AU-AIR datasets. Vehicle recognition, traffic density detection, traffic flow analysis, and pedestrian route generation are all possible uses for the proposed system. It can transform traffic management and improve overall road safety due to its powerful algorithms and cutting-edge technologies. The system's adaptability makes it a significant asset in modern transportation, from optimizing signal timing to improving pedestrian navigation.INDEX TERMS geo-referencing, random forest, particle filter, vehicle detection, tracking, and trajectories.

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Section: E Vehicle Detectionmentioning

confidence: 99%

Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach

Yusuf,

Hanzla,

Rahman

et al. 2024

IEEE Access

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“…The script LeNet CNN architecture were used and, Experiments revealed a classification accuracy of 98.8% for Hindi numerals in the literature. (25,26) of characters on number plates in Hindi. Some Pre-processing techniques have applied during pattern recognition in the aspect to train a machine.…”

Section: Jishnu Mukhoti Et Al Experimented On Handwritten Digit Class...mentioning

confidence: 99%

A Survey of Optical Character Recognition Techniques on Indic Script

Ch¹,

B²

2022

ECS Trans.

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Optical Character Recognition (OCR) is a technique that converts printed text and images into a digitized form, which can be manipulated by a machine. It has many application sectors like Banking, Financial, Legal applications, etc. Initially researchers were addressed and proposed many algorithms in image processing for character recognition and mapping. Most of the researchers focused on the Latin script English as it was supported by the Encoding standard ASCII. Later, people start realizing that OCR techniques for other languages are also gaining momentum these days. With the advent of technology and Unicode revolution, native language-based OCR solutions started emerging. In this paper, we aim to focus on the latest machine learning techniques applied on OCR for the language English and two languages from Indian continent were presented. Out of the two Indian languages, one is the stroke-based language, i.e. Hindi, and the other being cursive script-based language Telugu.

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“…It also allows you to do things like compressing data into ZIP files, incorporating them into a website, highlighting keywords, and connecting them to an email that isn't possible with physical copies. From the aforementioned information, it is important to conduct research on developing text recognition using OCR as it is effective in many real-world applications such as digital archiving, relying heavily on OCR [6], fully automated OCR-based electricity billing [7], automatic number plate recognition [8][9][10][11], handling bank cheques [12], etc.…”

Section: Uniform Backgroundmentioning

confidence: 99%

Development of text extraction technique using optical character recognition and morphological reconstruction to eliminate artifacts of image’s background

Jwaid

2022

EEJET

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Text recognition of images is beneficial in a wide range of computer vision purposes such as robot navigation, document analysis, and image search. The optical character recognition (OCR) technique presents a simple tool to combine text recognition functionality to many industrial and educational applications. Best OCR results can be acquired when the background of the text image is uniform and appears as a document picture. In contrast, the challenges to recognizing accurate texts occur when the image has a non-uniform background that require further preprocessing to obtain acceptable OCR result. This work discusses three scenarios. Initially, this work will test the OCR on a normal business card as an image with a uniform background. Next, discusses the text recognition of a keypad image including digits with a non-uniform background. Here, there are two preprocessing algorithms used to enhance the OCR function to overcome the negative effect of the non-uniform background of images and to detect text with high accuracy. Finally, the developed OCR method is tested on different scanned bills and discusses the variation of the obtained results. The two algorithms are the morphological reconstruction to eliminate artifacts and create cleaner images to be further processed by OCR and the Region of Interest ROI-based OCR to spot explicit regions in a tested image. Verification for the effectiveness of the Morphological-based OCR over the ROI-based method has been conducted on a dataset of scanned electricity bills images with an accuracy of 98.2 % for Morphological-based while it is only about 89.3 % for ROI-based OCR.

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Template Matching for Automatic Number Plate Recognition System with Optical Character Recognition

Cited by 12 publications

References 9 publications

Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach

Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach

A Survey of Optical Character Recognition Techniques on Indic Script

Development of text extraction technique using optical character recognition and morphological reconstruction to eliminate artifacts of image’s background

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