On Wednesday, Google announced that users can now instantly translate printed text into a total of 27 languages.
The search engine giant added 20 languages to their instant virtual translation app, a big leap from the original seven languages (English, French, German, Italian, Portuguese, Russian and Spanish) people could use to instantly identify words in a foreign language. The updates to Google Translate will reach Android and iPhone devices in the next couple of days, according to a Google blog post.
Additionally, if a user takes a photo of the text through camera mode, Google can translate the text into an additional 10 languages, for a total of 37.
Google’s secret to recognizing this vast array of languages is hidden in deep neural networks, the same networks that create hallucinative images and eerie art. By teaming up with Quest Visual, Google was “able to work with some of the top researchers in deep learning,” according to Google’s research blog.
The images undergo a step-by-step process, which begins when the app finds letters in the image through identifying blocks of pixels with similar colors next to each other. If the blocks form a continuous line, then the app believes the pixels are most likely part of a word.
But in real life, handwritten letters aren’t perfect clean lines of unadulterated pixels. Oftentimes letters are sloppily written, slightly smudged, and have traces of dirt or food around them. In order to have the app still recognize imperfect letters, the Google team created ”all kinds of fake 'dirt' to convincingly mimic the noisiness of the real world,” according to Google’s blog post.
After the program has found the letters, it then looks up approximate definitions to find the correct word, but also to account for mistakes that the program might have made earlier in the process. For instance, a handwritten “s” could have been interpreted as a “5” in the translation process, but the program would still ultimately translate the mistaken “5uper” as “super.”
But not everyone has a data processing center with the capabilities of Google. In fact, most of the time the phones and laptops we use run far below that computing power. To counter that issue, Google had to develop a small neural net to limit the information density that the computer processor handles.
This recent advance marks an innovative application of neural networks to a commonly used and practical application.
“Sometimes new technology can seem very abstract, and it's not always obvious what the applications for things like convolutional neural nets could be," notes the Google research blog. "We think breaking down language barriers is one great use.”
And Google promises that there more barriers to be broken: "More than half of the content on the Internet is in English, but only around 20% of the world’s population speaks English. Today’s updates knock down a few more language barriers, helping you communicate better and get the information you need."