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Beyond these methods, data extraction also involves **identifying and extracting structured data within images**. For example, in the case of a product image, the system might be able to extract price information, product names, or even descriptions directly from the image. This can involve techniques like object detection (to identify the objects of interest) combined with OCR (to extract the associated textual data). This level of data extraction is transforming retail, e-commerce, and other fields that rely on the analysis of visual data. For example, retailers can automatically extract pricing information from product images to update their inventory systems, thus saving time and reducing the chances of human error. Likewise, e-commerce businesses can extract product details from images to automatically populate product catalogs. This streamlined data extraction not only improves efficiency but also allows businesses to analyze and leverage visual data for advanced insights, such as understanding consumer preferences or identifying the latest market trends. The process is a combination of advanced algorithms, machine learning models, and human expertise to ensure accuracy and relevance.