Our second example deals with a more challenging problem: the recognition of hand-printed letters of the alphabet. The characters that people print in the ordinary course of filling out forms and questionnaires are surprisingly varied. Gaps abound wherecontinuous lines might be expected; curves and sharp angles appear interchangeably; there is almost every imaginable distortion of slant, shape and size. Even human readers cannot always identify such characters; their error rate is about 3 per cent on randomly selected letters and numbers, seen out of context.
– from Oliver G. Selfridge & Ulric Neisser. PATTERN RECOGNITION BY MACHINE . In Computers & thought, Edward A. Feigenbaum and Julian Feldman (Eds.). MIT Press, Cambridge, MA, USA, 1963. pp. 8-30.
This video course is a practical guide for developers who want to get started with building computer vision applications using Python 3. The video is divided into six sections: Throughout this video course, three image processing libraries: Pillow, Scikit-Image, and OpenCV are used to implement different computer vision algorithms. The course will help you build Computer Vision applications that are capable of working in real-world scenarios effectively. Some of the applications that we look at in the course are Optical Character Recognition, Object Tracking and building a Computer Vision as a Service platform that works over the internet. Saurabh Kapur is a computer science student at Indraprastha Institute of Information Technology, Delhi. His interests are in computer vision, numerical analysis, and algorithm design.
Not even the highest Roman Catholic church archivists know what's hiding in the archives' endless volumes, which are carefully stored near the Sistine Chapel. Only a tiny amount of these archives have been digitized–the rest is an endless ocean of inaccessible papers and parchments. Going through its tomes in search of something would be a task that not even the goddess Minerva herself would be able to accomplish. Some libraries have used technology to digitize their collections, like Optical Character Recognition software that's trained to recognize fixed, separated individual letter shapes. However, OCR is useless when it comes to the endless variety of free-flowing cursive styles featured in many of the Vatican's tomes, which go all the way back to the eighth century.
Somewhere within the Vatican exists the Vatican Secret Archives, whose 53 miles of shelving contains more than 600 collections of account books, official acts, papal correspondence, and other historical documents. Though its holdings date back to the eighth century, it has in the past few weeks come to worldwide attention. This has brought about all manner of jokes about the plot of Dan Brown's next novel, but also important news about the technology of manuscript digitization. It seems a project to get the contents of the Vatican Secret Archives digitized and online has made great progress cracking a problem that once seemed impossibly difficult: turning handwriting into computer-searchable text. In Codice Ratio is "developing a full-fledged system to automatically transcribe the contents of the manuscripts" that uses not the standard method of optical character recognition (OCR), which looks for the spaces between words, but a new way that can handle connected cursive and calligraphic letters.
In Italy, 120 high school students helped solve a centuries-old problem: how to give researchers access to the Vatican Secret Archives, a massive collection of documents detailing the Vatican's activities as far back as the eighth century. That should look pretty great on their college applications. The shelves of the Vatican Secret Archives are about 85 kilometers (53 miles) long and house 35,000 volumes of catalogues. But the documents that researchers have scanned and uploaded take up less than an inch. That's because the Vatican seems to not have wanted to share the information.
The Vatican Secret Archives comprise 600 collections of texts spanning 12 centuries, most of which are nearly impossible to access. The Atlantic reports that a team of scientists is hoping to change that with help from some high school students and artificial intelligence software. In Codice Ratio is a new research project dedicated to analyzing the vast majority of Vatican manuscripts that have never been digitized. When other libraries wish to make a digital archive of their inventory, they often use optical-character-recognition (OCR) software. Such programs can be trained to recognize the letters in a certain alphabet, pick them out of hard-copy manuscripts, and convert them to searchable text.
But a new project could change all that. Known as In Codice Ratio, it uses a combination of artificial intelligence and optical-character-recognition (OCR) software to scour these neglected texts and make their transcripts available for the very first time. If successful, the technology could also open up untold numbers of other documents at historical archives around the world. OCR has been used to scan books and other printed documents for years, but it's not well suited for the material in the Secret Archives. Traditional OCR breaks words down into a series of letter-images by looking for the spaces between letters.
Ludwig Wittgenstein once imagined that everyone had a box with something in it called a "beetle." Denying the possibility of private language, the philosopher wrote, "No one can look into anyone else's box, and everyone says he knows what a beetle is only by looking at his beetle." Wittgenstein meant that we learn a word by observing the rules governing its use, but no one sees another person's beetle: "It would be quite possible for everyone to have something different in his box," or nothing at all. An apparently intractable fact of life is that our thoughts are inaccessible to one another. Our skulls are like space helmets; we are trapped in our heads, unable to convey the quiddity of our sensations.
The debate on how artificial intelligence (AI) could shape the way we work tends to take place on a grand scale. We talk about a future where driverless vehicles will deliver our goods from factories filled with armies of robot workers. Adam Reynolds, CEO of webexpenses, discusses how we may be missing the more mundane and practical ways that AI is already reshaping our everyday working lives, and transforming the way businesses operate. Thanks to a new generation of AI-based systems and tools we can eliminate a whole swathe of tedious and repetitive work and home tasks – bringing intuition, help and time-saving benefits to our lives. These can be found within every industry – from systems designed to root out important clauses from large volumes of legal documents to medical software that identifies potential risks in patient data.
Scientists have developed an astonishing mind-reading machine which can translate what you are thinking and instantly display it as text. They claim that it has an accuracy rate of 90 per cent or more and say that it works by interpreting consonants and vowels in our brains. The researchers believe that the machine could one day help patients who suffer from conditions that don't allow them to speak or move. The machine registers and analyses the combination of vowels and consonants that we use when constructing a sentence in our brains. It interprets these sentences based on neural signals and can translate them into text in real time.