Research on License Plate Image Segmentation and Intelligent Character Recognition

With the accumulation of people’s wealth and the improvement of purchasing power, more and more people are buying cars as a means of travel. Walking and cycling of the past have now become a car trip. License plate recognition technology is especially important in intelligent transportation systems. It has been widely used in large shopping malls or supermarket parking lots, highway toll stations, speeding violation supervision and other fields. However, the accuracy and efficiency of license plate image recognition are insufficient. To solve the above problems, we propose a license plate character recognition method based on local HOG and layered LBP feature fusion from the perspectives of image pre-processing, license location, characters’ segmentation and recognition. First, pre-processing the license image area by highlighting the license plate image; then, the license plate is positioned based on wavelet decomposition and brightness moment; next the tilted license plate image is corrected, the license plate frame is adjusted, and characterization is performed by using the improved projection method based on the fact that the projection of the character is a single peak or a double peak. Finally, the local HOG and hierarchical LBP feature fusion methods are used to identify the license characters. The results show that the license plate’s character recognition rate of the proposed method reaches 99.71%, and the time taken is small. This not only improves the character recognition rate, but also saves recognition time. The results show that the method has important practical significance in license plates’ recognition.

Handwritten Gurmukhi Digit Prediction using Convolutional Neural Network with Keras and Tensorflow

Intelligent Character Recognition is a term which is specifically used for the recognition of handwritten character or digit. It is a prominent research area of computer vision field of machine learning or deep learning which trained the machine to analyze the pattern of handwritten character image and identify it. Recognition of handwritten character is a hard process because single person can handwrite the same text in number of ways by making a little variation in holding the pen. Handwriting has no specific font style or size. It differs person to person or more specifically it differs how one is holding the pen. Deep Leaning has brought the breakthrough performance in this research area with its dedicated models like Convolutional Neural Network, Recurrent Neural Network etc. In this paper, we have trained model with Convolutional Neural Network with different number of layers and filters over 10,559 handwritten gurmukhi digit images and validate over 1320 images. Consequently we could achieve the maximum accuracy of 99.24%.

Research and design of intelligent image recognition mechanism and its application

In this paper, an in-depth study on the recognition mechanism (identification experts) and behavioral function simulation is made, and a design method has been developed -----Image recognition mechanism. The research results and methods are expounded. At the same time, experiments are carried out on the above design methods. The effectiveness and feasibility of this design have been proved by experiments. Furthermore, the design of the expert observer is carried out. The design of artificial intelligent character recognition machine. Its ability to recognize complex background images has a high success rate, which has been proved by experiments, and its adaptive ability is very strong.