Computer Vision for Mobile Expense Scanning

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Azure's Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents - invoices, bills, financial reports, articles, and more.

For a mobile app that is used to scan and store expenses while traveling, the most suitable type of computer vision is Optical Character Recognition (OCR).

OCR is a computer vision technique that involves recognizing and extracting text from images. It is commonly used for digitizing physical documents, such as receipts, by converting the text on the document into machine-readable characters that can be stored electronically.

In the context of the mobile app for scanning and storing expenses, OCR can be used to extract information such as the date, vendor, amount, and other relevant details from the scanned receipts. This information can then be stored in a digital format for later use, such as submitting expense reports.

The other options, such as semantic segmentation, image classification, and object detection, are not as suitable for this task. Semantic segmentation involves dividing an image into distinct segments and assigning each segment a label. This technique is more suited for tasks such as image analysis or self-driving cars, but not for scanning receipts.

Image classification involves categorizing an image into predefined classes. This technique is more suited for tasks such as recognizing different types of objects in an image, but not for scanning receipts.

Object detection involves detecting and localizing objects within an image. This technique is more suited for tasks such as identifying people or objects within an image, but not for scanning receipts.

In summary, OCR is the most suitable computer vision technique for developing a mobile app that can scan and store expenses while traveling.