A SYSTEM BASED ON CHARACTER RECOGNITION OF VENEZUELAN LICENSE PLATES

Abstract.        We describe the development of a system based on character recognition for pictures of license plates. The system is based in an expert system, using a series of image processing techniques. A previous stage of image processing is required in order to enhance the data associated to the objects to be recognized and to filter any unwanted data. We propose an approach founded on segmentation techniques to isolate the objects of interest within the image. Character recognition starts with algorithms adapted to the features which the segmented regions present, concerning the characters to be recognized. The computational tool in which the model is executed has been developed in a multi-platform environment which uses C++ and Fast Light Toolkit (FLTK) as scheme of programing and user interface development respectively. The system has been applied to 39 license plates of Venezuelan vehicles, having 249 characters to recognize, obtaining an acceptance rate of 85.26%, within a range between 72% and 94% of acceptance for each character.

Keywords: Character recognition, expert system, segmentation, algorithm, multi-platform environment.

International Conference Publication ISBN: 978-980-7161-03-9 TCG pp. 25. CIMENICS 2012


This was my final project for the obtainment of my engineering degree in computer science, and my first and until now my only scientific paper. It consists of the development of a system in the domain of shape recognition.

The system has as a data entry a bmp format image, this can be in color or in black and white. And As an output the system give a collection of strings with the possible results with the average for each one of characters. The system is developed with the language C++ with a library called FLTK for the construction of the user interface.

Technically the first phase is the transformation of the image in gray scale and raw format, just a matrix 2x2 with the values for each pixel in a scale between 0 – 255, after that the processing consist in the application of a Gaussian filter whit different parameters according the ambient properties that the image got at the obtaining moment. The third phase consist in an application of morphologic filters for to get a binary image without lose data. After that the image is segmented using an algorithm called grounding regions which depends of the position of the initial Cartesian coordinates, for make this process better we used the properties of the image which is ever centered in the respect of the y axis, so we started a route from the middle in the y axis and the 0 coordinate in the x axis, sequentially in the horizontal direction the algorithm searched for a pixel which match with the threshold chose and start the grounding region, after finish with one character the algorithm positioned in the middle of the character regarding the y axis and the final coordinate of the last character regarding the x axis. The fifth phase consisted in the recognition of each region segmented, the system used an algorithm created by me which consisted in a transformation of each region in a vectorial function of two dimensions. For doing the process of recognition we used an algorithm of machine learning to charge the data base of vectorial functions which represented each character of the alphabet, the algorithm compared each component of the function and calculated a similarity error which it was used for the estimation of the more similar character.

San Cristobal - Venezuela, July - December 2009