Adaptive Thresholding for Image Segmentation and Text Extraction Technology

In this article, I will introduce adaptive thresholding techniques for image segmentation and text extraction, and explore how to implement these using MATLAB. This technology is highly beneficial for understanding fundamental principles as it provides insight into important concepts and techniques in the field of image processing. By employing adaptive thresholding methods, we can segment images into distinct regions based on varying image characteristics and pixel values, subsequently extracting textual information from these regions. This approach utilizes algorithms like Otsu's method or local thresholding techniques that dynamically calculate optimal threshold values for different image areas rather than using a global threshold. Key MATLAB functions for implementation include graythresh for global thresholding and custom implementations for local adaptive thresholding using functions like blockproc for processing image regions. The method finds applications in numerous fields including text recognition, image analysis, and computer vision. Therefore, learning and mastering this technology will provide significant assistance for research and applications in related domains, particularly through practical implementation using MATLAB's image processing toolbox.