More than a billion files are saved to Dropbox every day, and we need to run many asynchronous jobs in response to these events to power various Dropbox features. Examples of these asynchronous jobs include indexing a file to enable search over its contents, generating previews of files to be displayed when the files are viewed on the Dropbox website, and delivering notifications of file changes to third-party apps using the Dropbox developer API. This is where Cape comes in — it’s a framework that enables real-time asynchronous processing of billions of events a day,
In this post we will take you behind the scenes on how we built a state-of-the-art Optical Character Recognition (OCR) pipeline for our mobile document scanner. We used computer vision and deep learning advances such as bi-directional Long Short Term Memory (LSTMs), Connectionist Temporal Classification (CTC), convolutional neural nets (CNNs), and more. In addition, we will also dive deep into what it took to actually make our OCR pipeline production-ready at Dropbox scale.
It’s universally acknowledged that it’s a bad idea to store plain-text passwords. If a database containing plain-text passwords is compromised, user accounts are in immediate danger. For this reason, as early as 1976, the industry standardized on storing passwords using secure, one-way hashing mechanisms (starting with Unix Crypt). Unfortunately, while this prevents the direct reading of passwords in case of a compromise, all hashing mechanisms necessarily allow attackers to brute force the hash offline, by going through lists of possible passwords, hashing them, and comparing the result. In this context, secure hashing functions like SHA have a critical flaw for password hashing: they are designed to be fast.
A few weeks ago, Dropbox launched a set of new productivity tools including document scanning on iOS. This new feature allows users to scan documents with their smartphone camera and store those scans directly in their Dropbox. The feature automatically detects the document in the frame, extracts it from the background, fits it to a rectangular shape, removes shadows and adjusts the contrast, and finally saves it to a PDF file. For Dropbox Business users, we also run Optical Character Recognition (OCR) to recognize the text in the document for search and copy-pasting.
We are pleased to announce the open source release of Lepton, our new streaming image compression format, under the Apache license.
Lepton achieves a 22% savings reduction for existing JPEG images, by predicting coefficients in JPEG blocks and feeding those predictions as context into an arithmetic coder. Lepton preserves the original file bit-for-bit perfectly. It compresses JPEG files at a rate of 5 megabytes per second and decodes them back to the original bits at 15 megabytes per second, securely, deterministically, and in under 24 megabytes of memory.
We have used Lepton to encode 16 billion images saved to Dropbox,