## 1 Introduction  
The recognition of vehicle license plates and their alphanumeric content is a pressing necessity that has garnered considerable attention from researchers in recent decades. Various methods have emerged to address challenges such as detecting vehicle license plates in everyday environments and recognizing all their characters and digits under difficult conditions. Most methods are based on features tailored for vehicle license plates of specific regions, given the diverse design laws across countries regarding font type, number of digits, background color, text color, and even reflective properties.  
In general, techniques for detection and recognition fall into three categories: (i) methods for vehicle license plate detection and character segmentation, (ii) optical character recognition (OCR), and (iii) vehicle license plate recognition. The literature indicates that techniques utilizing deep learning yield superior results [1], though they rely heavily on the volume of training data. Satisfactory results are not achievable with small datasets, and processing time significantly increases with very large databases. Therefore, it is logical to develop models with region-specific databases, despite common characters across regions.  
In the case of Chile, alphanumeric vehicle license plates have been in circulation since 1985, comprising 2 letters (except for N and Q) and 4 numbers, separated by the national coat of arms logo. Since 2007, the format has changed to 4 letters and 2 numbers, with the set of letters restricted to 18. Additionally, there are 10 different styles denoting vehicle types, with common styles including a white background for private vehicles, orange for taxis, yellow for public transportation, and black for police vehicles.  
Despite advancements, challenges persist, such as processing time, environmental opacity, and necessary resolution per letter. Given these challenges, preprocessing images becomes crucial to increase success rates in vehicle license plate recognition. Hence, this paper explores the application of a bio-inspired parvocellular retinal model as a preprocessing stage to accentuate scene contours.  
Bio-inspired retinal processing emulates mammalian visual responses, extracting aspects such as contour, depth, and motion. With the application of a bio-inspired retina, photographs with insufficient optical analysis often improve, making it a relevant tool to enhance the success of artificial vision processes.  
The primary objective is to enhance the recognition of Chilean vehicle license plate characters, with secondary objectives including presenting key concepts of the bio-inspired retina and its mathematical modeling, and testing the retina model's efficacy in enhancing scenes for character recognition using OCR, convolutional neural networks (CNNs), and spiking neural networks (SNNs).  
This work is composed of six sections, beginning with the Introduction section and a general approach to the problem, followed by Section 2, entitled Related Work, where a bibliographic review of several works related to the application of the bio-inspired retina model is conducted. Then, in Section 3, the methodology is presented, with special emphasis on the mathematical models to be used. In Section 4, the final test results of the three experiments on 40 license plate samples under different conditions are reported. Section 5 presents a summary of the developed work and the most relevant conclusions. Finally, in Section 6, future work is presented.