Development of a neural network based software package for the automatic recognition of license plate characters
This research studies the techniques being used in character recognition. Software has been developed to automatically recognize license plate characters. The program is written in Borland C++ 2.0, and runs on Microsoft Windows 3.0. The system consists of two main parts: the preprocessor and the neural network. The preprocessor separates all characters from a picture. The neural network has a three-layer architecture using a back-propagation training method. Instead of using the derivative of the sigmoid transfer function, a differential step-size function is applied to the output neurons to solve local minima problems in training. The neural network recognizes the characters from the preprocessor. To run the system, an IBM 286 PC or compatible with at least 1M memory is required. An image digitizer system CapCalc was used to generate an image file in 512×480 format with 0-255 gray levels. It recognizes both white-on-black and black-on-white pictures. The pictures of the license plates should be fairly clear and complete. The neural network was trained with 145 characters from 24 license plate pictures of different characters and fonts. The pictures included 15 black-on- white and 9 white-on-black. The system was tested with additional 35 license plate pictures. It was able to recognize about half of the characters on the 5 pictures which met the requirements. On a 386 PS/2 model 70, it took about 16 seconds to recognize one picture. The system demonstrated the feasibility of automatically recognizing license plate characters using image-processing techniques and a neural network. This was an exploratory lab study, and was not intended to be a realistic model of a biological vision system.
School Location:USA - Ohio
Source Type:Master's Thesis
Keywords:neural network based software automatic recognition license plate characters
Date of Publication:01/01/1992