Recognition Design of License Plate and Car Type Using Tesseract Ocr and Emgucv

The goal of the research is to design and implement software that can recognize license plates and car types from images. The method used for the research is soft computing using library of EmguCV. There are four phases in creating the software, i.e., input image process, pre-processing, training processing and recognition. Firstly, user enters the car image. Then, the program reads and does pre-processing the image from bitmap form into vector. The next process is training process, which is learning phase in order the system to be able recognize an object (in this case license plate and car type), and in the end is the recognition process itself. The result is data about the car types and the license plates that have been entered. Using simulation, this software successfully recognized license plate by 80.223% accurate and car type 75% accurate

A novel license plate character segmentation method for different types of vehicle license plates.

License plate character segmentation (LPCS) is a very important part of vehicle license plate recognition (LPR) system. The accuracy of LPR system widely depends on two parts; namely license plate detection (LPD) and LPCS. Different country has different types and shapes of LPs are available. Based on character position on LP, we can find two types of LPs over the world, single row (SR) and double rows (DR) LP. Most of the LPCS methods are generally used for SRLP. This paper proposed a novel LPCS method for SR and DR types of LPs. Experimental results shows the real-time effectiveness of our proposed method. The accuracy of our proposed LPCS method is 99.05% and the average computational time is 27ms which is higher than other existing methods

Prevention of Unauthorized Transport of Ore in Opencast Mines Using Automatic Number Plate Recognition

Security in mining is a primary concern, which mainly affects the production cost. An efficiently detecting and deterring theft will maximize the profitability of any mining organization. Many illegal transportation cases were registered in spite of rules imposed by central and state governments under Section 23 (c) of MMDR Act 1957. Use of an automated checkpoint gate based on license plate recognition and biometric fingerprint system for vehicle tracking enhances the security in mines. The method was tested on the number plates with various considerations like clean number plates, clean fingerprints, dusty and faded number plates, dusty fingerprints, and number plates captured by varying distance. By considering all the above conditions the pictures were processed by ANPR and bio-metric fingerprint modules. Vehicle license number plate was captured using a digital camera and the captured RGB image was converted to grayscale image. Thresholding was done to remove unwanted areas from the grayscale image. The characters of the number plate were segmented using Gabor filter. A track-sector matrix was generated by considering the number of pixels in each region and was matched with existing template to identify the character. The fingerprint scans the finger and matches with the template created at the time of fingerprint registration at the machine. The micro-controller accepted the processed output in binary form from ANPR and bio-metric fingerprint system. The micro-controller processed the binary output and the checkpoint gate was closed/open based on the output provided by the microcontroller to motor driver

Smart Data Recognition System For Seven Segment LED Display

The automatic data capturing system provides an alternative and effective way of data collection instead of manual data collection in the laboratory, especially for experiments that need to be carried out for a long period. It can solve common mistakes made by humans, like misreading or mistyping data. Thus, a new smart data recognition system for a seven-segment LED display is developed to sort the whole process of data collection to become more systematic and accurate. An image is captured and saved automatically in an image file, and then it is processed through MATLAB software to identify the digits displayed on the LED display. Once the image is preprocessed, analyzed, and recognized, the final output values obtained are transferred to an existing Excel file for a further process according to the user’s requirement. From the results obtained, it was proven that binary thresholding is the best preprocessing method, and the brightness of the image should be set to ‘0’ for better recognition output

Automatic face and VLP’s recognition for smart parking system

One of the concerning issues regarding smart city is Smart Parking. In Smart Parking, some researchers try to provide solutions and breakthroughs on several research topics among security systems, the availability of single space, an IoT framework, etc. In this study, we proposed a security system on Smart Parking based on face recognition and VLP’s (Vehicle License Plates) identification. In this research, SSIM (Structural Similarity) method as part of IQA has been applied due to its reliability and simple computation for face detection and recognition process. From the test results of 30 data, obtained the highest SSIM value 0.83 with the highest accuracy rate of 76.67%. That level of accuracy still has not reached the implementation standard of 99.9%. So that it still needs to be improved in the future studies, especially in the filtering noise section

Segmenting characters from license plate images with little prior knowledge

In this paper, to enable a fast and robust system for automatically recognizing license plates with various appearances, new and simple but efficient algorithms are developed to segment characters from extracted license plate images. Our goal is to segment characters properly from a license plate image region. Different from existing methods for segmenting degraded machine-printed characters, our algorithms are based on very weak assumptions and use no prior knowledge about the format of the plates, in order for them to be applicable to wider applications. Experimental results demonstrate promising efficiency and flexibility of the proposed scheme. © 2010 IEEE

Efficient Two-Step Approach for Automatic Number Plate Detection

intelligent transportation systems are rapidly growing mainly due to active development of novel hardware and software solutions. In the paper a problem of automatical number plate detection is considered. An efficient two-step approach based on plate candidates extraction with further classification by neural network is proposed. Stroke width transform and contours detection techniques are utilized for the image preprocessing and extraction of regions of interest. Different local feature sets are used for the final number plate extraction step. Efficiency of the developed method is tested with real datasets

Vehicle Plate Number Detection and Recognition Using Improved Algorithm

The growing Tanzanian population currently estimated to be 48 Million people and their use of vehicles as means of transport has kept increasing making enforcing traffic rules and regulations among road users a major challenge. This calls for a need to have an automated system that monitors the motorists with a pre-defined sense of intelligence. A Vehicle Detection and Recognition Algorithm which can provide automated access to relevant information to a number plate from information systems containing and managing databases on vehicle and their movements is required. This paper presents work on developed algorithm that localizes plate area, extract and segment character, and finally recognizes and interprets registration number from vehicle image. MATLAB R2012b Simulation software with Image Processing toolbox is employed. HSV color space image, morphological and statistical analysis operations were integrated and employed to a vehicle image to compute plate number area. In segmentation the properties like aspect ratio, extent, and area ratio were important measurement parameters. Finally, the template matching database and statistical character extracted from car image was correlated to recognize alphanumeric character to deduce car registration number.   Keywords— Character extraction, Detection algorithm, Recognition algorithm, Morphological matching, Template matchin