Once we confirmed that there was indeed strong user demand for the mobile document scanner and OCR, we decided to build our own in-house OCR system for several reasons.įirst, there was a cost consideration: having our own OCR system would save us significant money as the licensed commercial OCR SDK charged us based on the number of scans. This meant integrating the commercial system into our scanning pipeline, offering both features above to our business users to see if they found sufficient use from the OCR. When we built the first version of the mobile document scanner, we used a commercial off-the-shelf OCR library, in order to do product validation before diving too deep into creating our own machine learning-based OCR system. Create a hidden overlay so text can be copied and pasted from the scans saved as PDFs.Extract all the text in scanned documents and index it, so that it can be searched for later.Once OCR is run, we can then enable the following features for our Dropbox Business users: This process extracts actual text from our doc-scanned image. Hence the need to apply Optical Character Recognition, or OCR. Our mobile document scanner only outputs an image - any text in the image is just a set of pixels as far as the computer is concerned, and can’t be copy-pasted, searched for, or any of the other things you can do with text. The document scanner makes it possible to use your mobile phone to take photos and "scan" items like receipts and invoices. In previous posts we have described how Dropbox’s mobile document scanner works.
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