![]() We use a wide variety of safety precautions with such user-donated data, including never keeping donated data on local machines in permanent storage, maintaining extensive auditing, requiring strong authentication to access any of it, and more.Īnother important, machine learning-specific component for user-donated data is how to label it. At Dropbox, we take user privacy very seriously and thus made it clear that this was completely optional, and if donated, the files would be kept private and secure. To gather this set, we asked a small percentage of users whether they would donate some of their image files for us to improve our algorithms. We began by collecting a representative set of donated document images that match what users might upload, such as receipts, invoices, letters, etc. Our initial task was to see if we could even build a state of the art OCR system at all. We will take you through each of these steps in turn.
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