More specifically, we wanted to answer the following questions: This would not only inform the cost estimate, but also confirm its usefulness. Automatic image text recognition is able to intelligently distinguish between all of these documents to categorize data contained within.įirst, we set out to gauge the size of the task, specifically trying to understand the amount of data we would have to process. PDF files fall in-between because they can contain a mixture of text and image content. Image formats (like JPEG, PNG, or GIF) are generally not indexable because they have no text content, while text-based document formats (like TXT, DOCX, or HTML) are generally indexable. Similarly, 25% of these PDFs are scans of documents that are also candidates for automatic text recognition.įrom a computer vision perspective, although a document and an image of a document might appear very similar to a person, there’s a big difference in the way computers see these files: a document can be indexed for search, allowing users to find it by entering some words from the file an image is opaque to search indexing systems, since it appears as only a collection of pixels. These are now candidates for automatic image text recognition. Of those files, 10-20% are photos of documents-like receipts and whiteboard images-as opposed to documents themselves. People have stored more than 20 billion image and PDF files in Dropbox. The potential benefit of automatically recognizing text in images (including PDFs containing images) is tremendous. One of the most impactful benefits that users will see from these changes is that users on Dropbox Professional and Dropbox Business Advanced and Enterprise plans can search for English text within images and PDFs using a system we’re describing as automatic image text recognition. In our previous blog post s, we talked about how we updated the Dropbox search engine to add intelligence into our users’ workflow, and how we built our optical character recognition (OCR) pipeline.
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