Google Research Blog
The latest news from Research at Google
Sergey and Larry awarded the Seoul Test-of-Time Award from WWW 2015
Friday, May 22, 2015
Posted by Andrei Broder, Google Distinguished Scientist
Today, at the
24th International World Wide Web Conference
(WWW) in Florence, Italy, our company founders, Sergey Brin and Larry Page, received the inaugural
Seoul Test-of-Time Award
for their 1998 paper “
The Anatomy of a Large-Scale Hypertextual Web Search Engine
”, which introduced Google to the world at the
7th WWW conference in Brisbane, Australia
. I had the pleasure and honor to accept the award on behalf of Larry and Sergey from
Professor Chin-Wan Chung
, who led the committee that created the award.
Except for the fact that I was myself in Brisbane, it is hard to believe that Google began just as a two-student research project at Stanford University 17 years ago with the goal to “produce much more satisfying search results than existing systems.” Their paper presented two breakthrough concepts: first, using a distributed system built on inexpensive commodity hardware to deal with the size of the index, and second, using the hyperlink structure of the Web as a powerful new relevance signal. By now these ideas are common wisdom, but their paper continues to be very influential: it has over
so far and more are added every day.
Since those beginnings Google has continued to grow, with tools that enable
small business owners to reach customers
help long lost friends to reunite
empower users to discover answers
. We keep pursuing new ideas and products, generating discoveries that both affect the world and advance the state-of-the-art in Computer Science and related disciplines. From products like
Google Earth Engine
to advances in
Natural Language Understanding
, it is our continuing goal to create useful tools and services that benefit our users.
Larry and Sergey sent a video message to the conference expressing their thanks and their encouragement for future research, in which Sergey said “There is still a ton of work left to do in Search, and on the Web as a whole and I couldn’t think of a more exciting time to be working in this space.” I certainly share this view, and was very gratified by the number of young computer scientists from all over the world that came by the Google booth at the conference to share their thoughts about the future of search, and to explore the possibility of joining our efforts.
Tone: An experimental Chrome extension for instant sharing over audio
Tuesday, May 19, 2015
Posted by Alex Kauffmann, Interaction Researcher, and Boris Smus, Software Engineer
Sometimes in the course of exploring new ideas, we'll stumble upon a technology application that gets us excited.
is a perfect example: it's a
extension that broadcasts the URL of the current tab to any machine within earshot that also has the extension installed. Tone is an experiment that we’ve enjoyed and found useful, and we think you may as well.
As digital devices have multiplied, so has the complexity of coordinating them and moving stuff between them. Tone grew out of the idea that while digital communication methods like email and chat have made it infinitely easier, cheaper, and faster to share things with people across the globe, they've actually made it more complicated to share things with the people standing right next to you. Tone aims to make sharing digital things with nearby people as easy as talking to them.
The first version was built in an afternoon for fun (which resulted in numerous
), but we increasingly found ourselves using it to share documents with everyone in a meeting quickly, to exchange design files back and forth while collaborating on UI design, and to contribute relevant links without interrupting conversations.
Tone provides an easy-to-understand broadcast mechanism that behaves like the human voice—it doesn't pass through walls like radio or require pairing or addressing. The initial prototype used an efficient audio transmission scheme that sounded terrible, so we played it beyond the range of human hearing. However, because many laptop microphones and nearly all video conferencing systems are optimized for voice, it improved reliability considerably to also include a minimal
-based audible codec. The combination is reliable for short distances in the majority of audio environments even at low volumes, and it even works over Hangouts.
Because it's audio based, Tone behaves like speech in interesting ways. The orientation of laptops relative to each other, the acoustic characteristics of the space, the particular speaker volume and mic sensitivity, and even where you're standing will all affect Tone's reliability. Not every nearby machine will always receive every broadcast, just like not everyone will always hear every word someone says. But resending is painless and debugging generally just requires raising the volume. Many groups at Google have found that the tradeoffs between ease and reliability worthwhile—it is our hope that small teams, students in classrooms, and families with multiple computers will too.
To get started, first install the
Tone extension for Chrome
. Then simply open a tab with the URL you want to share, make sure your volume is on, and press the Tone button. Your machine will then emit a short sequence of beeps. Nearby machines receive a clickable notification that will open the same tab. Getting everyone on the same page has never been so easy!
Paper to Digital in 200+ languages
Wednesday, May 06, 2015
Posted by Dmitriy Genzel and Ashok Popat, Research Scientists and Dhyanesh Narayanan, Product Manager
Many of the world’s important sources of information - books, newspapers, magazines, pamphlets, and historical documents - are not digital. Unlike digital documents, these paper-based sources of information are difficult to search through or edit, or worse, completely inaccessible to some people. Part of the solution is
, getting a digital image of the page, but raw image pixels aren’t yet recognized as textual content from the computer’s point of view.
Optical Character Recognition
(OCR) technology aims to turn pictures of text into computer text that can be indexed, searched, and edited. For some time,
has provided OCR capabilities. Recently, we expanded this state-of-the-art technology to support all of the world’s major languages - that’s over
in more than 25 writing systems. This technology is available to users in 2 easy steps:
1. Upload a scanned document in its current form (say, as an image or PDF). The example below shows a scanned document in Hindi uploaded to a user’s Drive account as a PNG.
2. Right-click on the document in the Drive interface, and select ‘Open with’ -> ‘Google Docs’.
This opens a Google document with the original image followed by the extracted text.
You don’t even need to specify which language the document is in; the system will determine that automatically. Or, you can use the
Google Drive API
for more explicit control over the language detection in documents. For example, here is an invocation of the Drive API in Python:
The OCR capability in Drive is also available in the
Drive App for Android
To make this possible, engineering teams across Google pursued an approach to OCR focused on broad language coverage, with a goal of designing an architecture that could potentially work with all existing languages and writing systems. We do this in part by using
Hidden Markov Models
(HMMs) to make sense of the input as a whole sequence, rather than first trying to break it apart into pieces. This is similar to how modern
speech recognition systems
recognize audio input.
OCR and speech recognition share some challenges - like dealing with background “noise,” different languages, and low-quality inputs. But some challenges are specific to OCR: the variety of typefaces, the different types of scanners and cameras, and the need to work on older material that may contain archaic
and linguistic elements. In addition to utilizing HMMs, we leveraged many of the same technologies used in the
Google Handwriting Input
app to allow automatic learning of features and to give preference to more likely output, as well as
to allow effective combination of multiple sources of information, and modern methods in machine learning to minimize manual design and maximize use of data. We also take advantage of advances in internationalization and typesetting, by using synthetic data in our training.
Currently, the OCR works best on cleanly scanned, high-resolution documents in the most commonly used typefaces. We are working to improve performance on poor quality scans and challenging text layouts. Give it a try and
let us know
how it works for you.
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