Abstract
The image processing method involves several critical steps, with image preprocessing being particularly significant. Segmentation and contour extraction on digital images are essential in fields ranging from image recognition to image enhancement in various recording devices, such as photo and video cameras. This research identifies and analyzes the main drawbacks of existing segmentation and contour extraction methods, focusing on object recognition. Not all filters effectively remove noise; some may clear areas of interest, affecting gesture recognition accuracy. Therefore, studying the impact of image preprocessing on gesture recognition outcomes is crucial for improving pattern recognition performance through more efficient preprocessing methods. This study seeks to find an optimal solution by detecting specific features during the preprocessing stage that directly influence gesture recognition accuracy. This research is a key component of the AP19175452 project, funded by the ministry of science and higher education. The project aims to create automated interpretation systems for Kazakh sign language, promoting inclusivity and technological innovation in communication aids. By addressing these challenges, the study contributes to the development of more robust and adaptive image preprocessing techniques for gesture recognition systems.