Optical Character Recognition

Researchers at Facebook offered up a summary of a system they call "Rosetta," a machine learning approach that boosts traditional optical character recognition, or "OCR," to mine the hundreds of millions of photos uploaded to Facebook daily. Say you want to search for memes in images on Facebook: The site's challenge is to detect whether there are letters printed within an image, and then parse those letters to know what a phrase says. This technology has, of course, been in use for document processing for ages, but the challenge at Facebook was both to recognize text in any number of complex images, including text laid over the image, as in an internet meme or text such as a sign that was part of the original image, and then to make it work at the scale of the site's constant stream of images. Facebook researchers Fedor Borisyuk, Albert Gordo, and Viswanath Sivakumar shared the work on Rosetta at the Knowledge Discovery and Data Mining conference in London in late August, in a formal paper, and today, two of the authors, Gordo and Sivakumar, along with Facebook's Manohar Paluri, offered up a somewhat simpler blog post describing the work. Facebook split up the task of "extracting" text from an image into two separate matters, that of first detecting whether there is text at all in an image, and then to parsing what that word of phrase might be.

People online tend to communicate not just with words, but also with images. For a platform like Facebook with over 2 billion monthly active users, that means a plethora of images gets posted every day, including memes. In order to include images with text in relevant photo search results, to give screen readers a way to read what's written on them and to make sure they don't contain hate speech and other words that violate the website's content policy, Facebook has created and deployed a large-scale machine learning system called "Rosetta." Facebook needed an optical character recognition system that can regularly process huge volumes of content, so it had to conjure up its own technology. In a new blog post, the company explained how Rosetta works: it starts by detecting rectangular regions in images that potentially contain text.

Effect.AI published a new video blog informing the community about some of the latest developments regarding the project. Effect.AI's vision for its'Mechanical Turk', microtask platform is simply inspiring as its leadership is clearly choosing to build Effect.AI in a socially responsible way. Let's recap what the project is about, have a listen to what Polina Boykova has to say and connect some of the dots regarding the project's ambitions. Effect.AI has a three-staged approach with regards to building, what they call, the'Effect Network'. In the first and current stage, Effect.AI is aiming at disintermediating and decentralizing crowdsourced'micro-tasking'.

Google Cloud Text-to-Speech enables developers to synthesize natural-sounding speech with 30 voices, available in multiple languages and variants. It applies DeepMind's groundbreaking research in WaveNet and Google's powerful neural networks to deliver high fidelity audio. With this easy-to-use API, you can create lifelike interactions with your users, across many applications and devices.

Google Cloud on Tuesday announced the general availability of its Cloud Text-to-Speech API, which lets developers add natural-sounding speech to their devices or applications. The API also now offers a feature to optimize the speech for specific kinds of speakers. Google has also added several new WaveNet voices to the API, opening up opportunities for natural-sounding speech in more languages and a wider variety of voices. Google first announced Text-to-Speech in March, illustrating how Google has been able to leverage technology from its acquisition of DeepMind. The AI company created WaveNet, a deep neural network for generating raw audio.

Early experiments in computer vision took place in the 1950s, using some of the first neural networks to detect the edges of an object and to sort simple objects into categories like circles and squares. In the 1970s, the first commercial use of computer vision interpreted typed or handwritten text using optical character recognition. This advancement was used to interpret written text for the blind. As the internet matured the 1990s, making large sets of images available online for analysis, facial recognition programs flourished. These growing data sets helped make it possible for machines to identify specific people in photos and videos.

Twilio is giving developers more control over their interactive voice applications with built-in support for Amazon Polly -- the AWS text-to-speech service that uses deep learning to synthesize speech. The integration adds more than 50 human-sounding voices in 25 languages to the Twilio platform, the cloud communications company announced Monday. In addition to offering access to different voices and languages, Polly will enable developers using Twilio's Programmable Voice to control variables like the volume, pitch, rate and pronunciation of the voices that interact with end users. Programmable Voice has long offered a built-in basic text-to-speech (TTS) service that supports three voices, each with their own supported set of languages. TTS capabilities, however, have improved dramatically in recent years, and Twilio notes that Amazon has been at the forefront of these improvements.

Supervised learning needs labels, or annotations, that tell the algorithm what the right answers are in the training phases of your project. In fact, many of the examples of using MXNet, TensorFlow, and PyTorch start with annotated data sets you can use to explore the various features of those frameworks. Unfortunately, when you move from the examples to application, it's much less common to have a fully annotated set of data at your fingertips. This tutorial will show you how you can use Amazon Mechanical Turk (MTurk) from within your Amazon SageMaker notebook to get annotations for your data set and use them for training. TensorFlow provides an example of using an Estimator to classify irises using a neural network classifier.

These days, there is no part of our lives that is unaffected via computerization. A few illustrations incorporate clothes washers, microwaves, autopilot mode for autos and planes, Nestlé utilizing Robots to offer espresso units in stores in Japan, Walmart testing automatons to convey items in the US, our bank checks being arranged to utilize Optical Character Recognition (OCR), and ATMs. Automation, in basic words, is innovation that arrangements with the utilization of machines and PCs to the generation of merchandise and enterprises. This aids in completing works with practically no human help. With the appearance of PCs, numerous product frameworks were created to achieve assignments that were beforehand done on paper to oversee organizations, or not being done at all because of the absence of devices.

Arcticsid asked about turning text into a .jpg. I'll also explain converting an image back into text. Your browser will select the word, and then you'll be able to copy and paste it into your word processor or email program. But try double-clicking a word in the picture above (or in any of the other pictures in this article). In the digital world, there's a big difference between real text and an image that looks like text--even if it's not always obvious to the user.