Application of Artificial Intelligence Recognition Technology in Digital Image Processing

Synthetic Artificial Intelligence technique is a science and technique derived and developed on the basis of calculator application technology. Image recognition is a special image processing step that plays an important role. Only after image recognition can it enter the stage of picture analysis and understanding. With the development of various computer technologies, images have gradually become and have become an important source of information for people. The use of calculator artificial intelligence is becoming increasingly widespread; therefore, understanding its application and related research is more conducive to pointing out the direction of research and learning for us. The goal of this paper is to discuss the emergence and development of synthetic intelligence identification technology and analyze the application bottlenecks of various types of synthetic intelligence identification technology, so as to increase our understanding of Synthetic Artificial Intelligence technique and provide reference for the research in related fields. This article simply introduces the technology of artificial intelligence type and its new development trend, and by combining concrete images of public facilities, the application of different computer artificial recognition methods of image recognition processing on the basis of the traditional method is improved, and through the corresponding simulation software of processing and identification methods for the analysis and comparison, the main application of two methods, the image processing recognition error rate is less than 0.5; improving computer artificial intelligence identification technique for the analysis of its application in image processing has certain help. The preprocessing process generally includes image digitization, grayscale, binarization, noise removal, and character segmentation. In terms of image recognition, algorithms mainly include statistical recognition, syntax recognition, and template matching. In recent years, with the development of neural networks and support vector machine technology, image recognition technology has a new and higher level of development.