Authors: Pranav Chaudhari, Rushika Chaudhari, Damini Dubey, Vaishnavi Shinde
Certificate: View Certificate
Efficiently overseeing adherence to traffic regulations is an arduous task faced by authorities in light of population growth and increased vehicular activity on roads. The conventional approach to managing road users who break laws involves time-intensive manual processes that interfere with smooth transportation operations. This paper proposes a promising tactic for automating the production of E-challans through incorporation of Automatic Number Plate Recognition (ANPR) technology. By installing mounted cameras with ANPR capabilities alongside CCTV equipment which utilizes image processing together with optical character recognition technology (OCR), swift automated identification of infringing drivers can be achieved through read-outs from their vehicle registration plates. Ultimately, this method seeks to lessen reliance on human resources while enhancing the effectiveness of law enforcement activities.
The escalating population has led to a substantial increase in the number of vehicles on our roads, creating a pressing need for effective solutions in managing traffic rule violations. These violations encompass a wide range of offenses, including running red lights, failure to comply with seat belt and helmet regulations, speeding, inadequate vehicle insurance, fitness, pollution control, and driver's license compliance, among others. In response to the growing disregard for traffic regulations, the concept of challan has gained significant prominence.
However, authorities face considerable challenges in tracking such infractions, identifying vehicle owners, and enforcing appropriate penalties .
To address these challenges, the implementation of an electronic challan production system with automatic number plate identification has proven to be an invaluable tool for authorities. This system enables efficient handling of traffic rule violations, while providing offenders with a streamlined method of managing their penalties through the use of E-challan.
The primary objective of this project is to develop an electronic challan system that utilizes license plate recognition technology to accurately capture passing vehicles' license plates. Subsequently, a challan will be generated and sent to the registered owner's mobile number via SMS, which is associated with the vehicle's registration number.
This process involves the use of cameras to capture images of the violating vehicles, followed by number plate detection and character segmentation performed using an Automatic Number Plate Recognition (ANPR) system. The detected number plate is then matched against the database .
By employing this advanced real-time machine intelligent system, the proposed solution outlined in this paper ensures precise identification of the vehicle's number plate and generates an e-challan in the registered owner's name by identifying the specific traffic violation committed.
Consequently, this automated process significantly reduces the burden of manual work for officials, simplifies the management and monitoring of challans, and ensures more effective enforcement of traffic regulations.
II. RELATED WORK
III. PROPOSED SYSTEM
6. Challan Maker: The Challan Maker system utilizes optical character recognition (OCR) to extract characters from the number plate. These characters are then compared with the database to retrieve vehicle and owner information. The system verifies and checks the obtained details, and generates a challan .
7. Desktop Application: Users can conveniently access the Desktop application. For first-time visitors, it is necessary to create a new account. Once registered, users can log in and access their personalized dashboard. Within the dashboard, users can easily find their challan by entering their Username. They can make payments for the challans.
A. Optical Character Recognition (OCR)
OCR, also known as text recognition, is a program that extracts and repurposes data from scanned documents, camera images, and image-only PDFs. It identifies individual letters on the image, combines them into words, and then constructs sentences, enabling access to and editing of the original content. OCR eliminates the need for manual data entry. OCR systems utilize a combination of hardware and software to convert physical, printed documents into machine-readable text. Hardware components such as optical scanners or specialized circuit boards are used to copy or read the text, while software handles the advanced processing. With the integration of artificial intelligence (AI), OCR software can implement more sophisticated techniques for intelligent character recognition (ICR), such as identifying different languages or styles of handwriting. The OCR process is commonly employed to convert hard copy legal or historical documents into PDF format, enabling users to edit, format, and search the documents as if they were created with a word processor.
B. Convolution Neural Network (CNN)
CNN is a powerful architecture for image processing and pattern recognition. It consists of two main layers: the feature extraction layer and the feature map layer. In the feature extraction layer, each neuron's input is connected to local receptive fields in the previous layer, allowing it to extract local features. These local features capture the positional relationships between different features in the input image. The feature map layer comprises multiple computing layers, each composed of feature maps. A feature map represents a two-dimensional plane, where the weights of the neurons within the plane are the same. By utilizing the sigmoid function as the activation function in the convolution network, the feature maps exhibit distinctive shifts. Additionally, the weight sharing among neurons in the same mapping plane reduces the number of free parameters in the network.
Each convolution layer is followed by a computing layer, which is responsible for finding local averages and performing the second stage of feature extraction. This unique two-step feature extraction structure helps to decrease the resolution of the input.
C. The Convolution Layer
Is the initial layer in a CNN and plays a crucial role in extracting features from the input image, such as a leaf image. By convolving the image with different filters, the convolution layer learns image features by analyzing small squares of input data. This process allows for operations like edge detection, blur, and sharpening by applying filters such as identity filter, edge detection filter, sharpening filter, box blur filter, and Gaussian blur filter.
D. Pooling Layers
Are employed to reduce the number of parameters when dealing with large images. Spatial pooling, also known as subsampling or down sampling, decreases the dimensionality of each feature map while retaining important information. Fully connected layer takes the feature map matrix and converts it into a vector representation (x1, x2, x3, etc.). These features are combined in the fully connected layers to create a model capable of making predictions or classifying the input data.
E. Softmax Classifier
Along with activation functions such as softmax or sigmoid, is applied to classify the outputs of the model. This step is often used for tasks like leaf disease classification, where the model predicts the class or category of the input based on the extracted features.
Law enforcement, toll collection, vehicle theft detection, traffic regulation, and road safety, security and surveillance, as well as traffic management and monitoring, all stand to gain significant advantages from this project. One aspect that stands out is how this project streamlines the manual tasks carried out by authorities and officials, resulting in an accelerated process of inspecting and verifying vehicle details, ultimately enabling the efficient management of challans.
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Copyright © 2023 Pranav Chaudhari, Rushika Chaudhari, Damini Dubey, Vaishnavi Shinde. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.