Building Machine Learning Models for Classification of Text and Non-text Elements in Natural Scene Images

Abstract Computer vision aims to build autonomous systems that can perform some of the human visual system’s tasks (and even surpass it in many cases)among several applications Vision, extracting information from natural scene images is famous and influential. The gained an image vary identification, space measurements for navigation, or augmented reality applications. These contain relevant text elements as well non-text elements. Prior meaningful text, foremost task classify & correctly given images. present paper machine learning models accurately organizing benchmark dataset ICDAR 2013. result obtained terms confusion matrix determine overall accuracy different models.