Template Neural Particle Optimization For Vehicle License Plate Recognition

The need for vehicle recognition has emerged from cases such as security, smart toll collections and traffic monitoring systems. This type of applications produces high demands especially on the accuracy of license plate recognition (LPR). The challenge of LPR is to select the best method for recognizing characters. Since the importance of LPR arises over times, there is a need to find the best alternative to overcome the problem. The detection and extraction of license plate is conventionally based on image processing methods. The image processing method in license plate recognition generally comprises of five stages including pre-processing, morphological operation, feature extraction, segmentation and character recognition. Pre-processing is an initial step in image processing to improve image quality for more suitability in visualizing perception or computational processing while filtering is required to solve contrast enhancement, noise suppression, blurry issue and data reduction. Feature extraction is applied to locate accurately the license plate position and segmentation is used to find and segment the isolated characters on the plates, without losing features of the characters. Finally, character recognition determines each character, identity and displays it into machine readable form. This study introduces five methods of character recognition namely template matching (TM), back-propagation neural network (BPNN), Particle Swarm Optimization neural network (PSONN), hybrid of TM with BPNN (TM-BPNN) and hybrid of TM with PSONN (TM-PSONN). PSONN is proposed as an alternative to train feed-forward neural network, while TM-BPNN and TM-PSONN are proposed to produce a better recognition result. The performance evaluation is carried out based on mean squared error, processing time, number of training iteration, correlation value and percentage of accuracy. The performance of the selected methods was analyzed by making use real images of 300 vehicles. The hybrid of TM-BPNN gives the highest recognition result with 94% accuracy, followed by the hybrid of TM-PSONN with 91.3%, TM with 77.3%, BPNN with 61.7% and lastly PSONN with 37.7%

Real time mobile based license plate recognition system with neural networks

In this paper, the implementation of localizing and recognizing license plate in real time environment with a neural network using a mobile device is described. The neural networks used in this research are Convolutional Neural Network (CNN) and Backpropagation Feed Forward Neural Network (BPFFNN). Image processing algorithm for pre-processing, localization and segmentation is chosen based on its ability to cope with limited computational resource in mobile device. The proposed license plate localization steps include combination of Sobel edge detection method and morphological based method. Detected license plate image is segmented using connected component analysis (CCA) and bounding box method. Each cropped character is fed into CNN or BPFFNN model for character recognition process. The neural network model was pretrained using desktop computer and then later exported and implemented in Android mobile device. The experiment was conducted in a moving vehicle on selected driving routes. The results obtained showed that CNN performed better compared to BPFFNN in a real time environment

Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates

In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP) in combination with Ensemble Neural Network (ENN). We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN). This research aims to solve four main issues; (1) localization of car plates that has the same colour with the vehicle colour, (2) detection and recognition of car plates with varying sizes, (3) detection and recognition of car plates with different font types, and (4) detection and recognition of non-standardized car plates. The non-standardized Malaysian’s car plates are different from the normal plate as they contain italic characters, a combination of cursive characters, and different font types. The experimental results show that the combination of backpropagation and ENN can be effectively used to solve these four issues. The combination of BPP and ENN’s algorithm achieved a localization rate of 98% and a 97% in recognition rate. On the other hand, the combination of backpropagation and simple FFNN recorded a 96% recognition rate

Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection

في السنوات الأخيرة، كان هناك تطور مستمر في مجال تطبيق المركبات وعدد المركبات التي تتحرك على الطرق في جميع أنحاء البلاد. يعتبر تحديد رقم لوحة السيارة العربية بناءً على معالجة الصور مجالًا ديناميكيًا لهذا العمل ، وتم استخدام هذه التقنية لأغراض أمنية مثل تتبع السيارات المسروقة والتحكم في الوصول إلى المناطق المحظورة. يستخدم نظام تمييز اللوحات المرورية الكاميرا الرقمية لالتقاط صورة للسيارة متضمنة لوحة المرور وتعتبر كمدخل لنظام التعرف المقترح. يتكون النظام المقترح من ثلاث مراحل، تحديد لوحة ترخيص السيارة، تقسيم الاحرف والارقام الموجودة في الصورة الاساسية الى صور صغيرة تحتوي على (حرف– رقم) كلا على حدة ، والتعرف على الأحرف، يتم تحديد لوحة الرخصة  (LP) باستخدام خوارزمية كاني في الكشف على الحواف، وقد تم استخدام Connect Component Analysis (CCA) لتقسيم الحروف⸲ وأخيرًا يتم استخدام نموذج الشبكة العصبية الاصطناعية المتعددة الطبقات للتعرف على الرموز الموجودة في كل صورة، وبالتالي يتم عرض النتائج كنص على واجهة المستخدم الرسومية. وحقق النظام المقترح أداءً إجماليًا يبلغ 96 ٪ و 97.872 ٪  في تحديد لوحات المرور المتعددة الانماط والتعرف على الرموز العربية الموجودة في اللوحات على التوالي وفي ظل ظروف مختلفة.            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to identify and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully detects LP and recognizes multi-style Arabic characters with rates of 96% and 97.872% respectively under different conditions