This project explores EasyOCR’s performance with Latin characters under image degradation. Variables like character-background intensity difference, Gaussian blur, and relative character size were tested. EasyOCR excels in distinguishing unique lowercase and uppercase characters but tends to favor uppercase for similar shapes like C, S, U, or Z. Results showed that high character-background intensity differences affected OCR output, with confidence scores ranging from 3 % to 80%. Higher differences caused confusion between characters like o and 0, or i and 1. Increased Gaussian blur hindered recognition but improved it for certain letters like v. Image size had a significant impact, with character detection failing as sizes decreased to 40% to 30% of the original. These findings provide insights into EasyOCR’s capabilities and limitations with Latin characters under image degradation.

Image enhancement is an important topic in image analysis in order to help humans and computer vision algorithms to obtain an accuracy information for analysis. The visual quality and certain image properties, such as brightness, contrast, signal to noise ratio, resolution, edge sharpness, and colour accuracy were improved through the enhancement process. The goal of image enhancement is to improve the quality of an image to become more suitable for a particular application. Till today, numerous image enhancement methods have been proposed for various applications and efforts have been directed to further increase the quality of the enhancement results and minimize the computational complexity and memory usage. In this paper, an image enhancement method based on Discrete Cosine Transforms (DCT) and Discrete Wavelet Transform (DWT) was studied. This paper presents an exhaustive review of these studies and suggests a direction for future developments of image enhancement methods. Each method shows the owned advantages and drawbacks. In future, this work will give the direction to other researchers in order to propose new advanced enhancement techniques.

Retinal fundus image is important for the ophthalmologist to identify and detect many vision-related diseases, such as diabetes and hypertension. From an acquisition process, retinal images often have low gray level contrast and low dynamic range. This problem may seriously affect the diagnostic process and its outcome, especially if an automatic computer-based procedure is used to derive diagnostic parameters. In this paper, a new proposed method based on statistical information such as mean and standard deviation was studied. The combination of local and global technique was successful to detect the luminosity region. Then, a simple correction intensity equation was proposed in order to replace the problem intensity. The results of the numerical simulation (SNR = 2.347 and GCF = 4.581) indicate that the proposed method effective to enhance the luminosity region. Implications of the results and future research directions are also presented. Keywords: Detection, Luminosity, Retinal, Statistical.