Using Form Recognizer's prebuilt receipts model is the optimal approach for extracting merchant, date, and total information from scanned receipts due to its design specifically for this purpose. The prebuilt receipts model leverages machine learning techniques that have been trained on a diverse dataset of receipt formats. This means it can accurately identify and extract key information such as merchant names, transaction dates, and total amounts without the need for additional training or customization. This model is tailored to understand the structured layout of receipts, which generally include various fields in consistent locations, leading to high accuracy in recognition tasks. Utilizing such a specialized tool enhances efficiency by streamlining the process of data extraction, allowing developers to focus more on integrating this functionality into their applications rather than spending time on developing and training a custom solution. Choosing the Read API of the Computer Vision service or a layout service may provide some level of text recognition but would lack the advanced capability to interpret and categorize specific fields relevant to receipts. Similarly, implementing a custom recognition algorithm could lead to potential inefficiencies and require extensive resources to reach a level of accuracy comparable to the prebuilt receipts model. Thus, leveraging the prebuilt functionality not only saves time and effort but also ensures a more reliable outcome in extracting pertinent receipt data.