IMAGE RECOGNITION SYSTEM

Abstract

This paper presents an image recognition system that uses techniques from Machine Learning and Computer Vision to identify and classify objects in images. The system employs convolutional neural networks (CNNs) for feature extraction and accurate classification. Input images are preprocessed to enhance quality and consistency before being analyzed by the model. The system is trained on a dataset to achieve reliable performance, and its accuracy is evaluated using standard metrics. The proposed solution demonstrates effective results and can be applied in areas such as security, healthcare, and automation, highlighting the growing importance of intelligent visual recognition systems. With the development of machine learning for decades, there are still many problems unsolved, such as image recognition and location detection, image classification, image generation, speech recognition, natural language processing and so on. In the field of deep learning research, the research on image classification has always been the most basic, traditional and urgent research direction. At the same time, computer intelligent image recognition technology is also conducive to gradually better respond to the development of international indicators, and promote the development and progress of various fields. Therefore, image processing technology based on machine learning has been widely used in feature image, classification, segmentation and recognition, and is a hot spot in various fields. However, due to the complexity of video images and the distribution of objects in different application backgrounds, the classification accuracy becomes important and difficult. In the paper transportation industry, image recognition technology is applied to license plate recognition to extract license plate from complex background, segment license plate characters and recognize characters, and construct a machine learning non license plate automatic generation algorithm, which may improve the efficiency of non license plate recognition. The diversity and high generation speed of license plate training sample set can achieve the purpose of effectively training strong classifier. By using genetic algorithm to optimize BP neural network to classify license plate information, the anti-interference ability and license plate recognition accuracy are improved to a certain extent.