The identification of textual regions of interest (ROI) in images plays a crucial role in various applications, including document analysis, image understanding, and optical character recognition (OCR). This research focuses on developing effective methodologies for automatically detecting and extracting textual content from diverse images. The proposed approach leverages advanced computer vision and machine learning techniques to robustly identify and isolate textual regions within images. Initially, image preprocessing techniques are employed to enhance the quality and clarity of the input images. Subsequently, a combination of feature extraction and deep learning algorithms is applied to analyze the visual patterns associated with text. The model is trained on a diverse dataset to ensure its adaptability to various types of textual content, fonts, and backgrounds. Transfer learning techniques are explored to enhance the model's generalization capabilities, allowing it to perform well on unseen data. The training process involves optimizing the model parameters to achieve high accuracy and efficiency in ROI identification. To evaluate the proposed methodology, extensive experiments are conducted on benchmark datasets, comparing the performance against state-of-the-art methods. The results demonstrate the effectiveness of the proposed approach in accurately identifying textual ROIs across different scenarios, including complex backgrounds, varying fonts, and diverse image resolutions. Furthermore, the research explores potential applications of the identified textual ROIs, such as improving OCR systems, document summarization, and information retrieval. The robustness and adaptability of the proposed methodology position it as a valuable tool for text-related tasks in real-world applications. In conclusion, the developed approach showcases significant advancements in the identification of textual regions of interest in images, contributing to the broader field of computer vision and image processing. The potential impact of this research extends to fields such as automation, document analysis, and intelligent information extraction, fostering advancements in technology and enhancing the efficiency of various applications.

East methodology, Mser, Moron methodology Craft method, OCR