Hand gestures have evolved into a natural and intuitive means of engaging with technology. The objective of this research is to develop a robust system that can accurately recognize and classify hand gestures representing numbers. The proposed approach involves collecting a dataset of hand gesture images, preprocessing and enhancing the images, extracting relevant features, and training a machine learning model. The advancement of computer vision technology and object detection techniques, in conjunction with OpenCV's capability to analyze and comprehend hand gestures, presents a chance to transform the identification of numerical digits and its potential applications. The advancement of computer vision technology and object identification technologies, along with OpenCV's capacity to analyze and interpret hand gestures, has the potential to revolutionize human interaction, boosting people's access to information, education, and employment opportunities. Keywords: Computer Vision, Machine learning, Deep Learning, Neural Networks

翻译：手势已演变为一种自然直观的人机交互方式。本研究旨在开发一种能够准确识别并分类表示数字的手势的鲁棒系统。所提出的方法包括采集手势图像数据集、对图像进行预处理与增强、提取相关特征以及训练机器学习模型。计算机视觉技术与目标检测方法的进步，结合OpenCV分析和理解手势的能力，为数字识别及其潜在应用提供了变革契机。计算机视觉技术与目标识别方法的演进，配合OpenCV解析和诠释手势的功能，有望革新人机交互模式，从而提升人们在信息获取、教育及就业机会方面的可及性。关键词：计算机视觉，机器学习，深度学习，神经网络