Handwriting Recognition

High school students helped an AI learn to read old handwritten texts


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.

Microsoft's digital ink tool makes sense of your chickenscratch


During its Build conference today, Microsoft introduced Project Ink Analysis, which does exactly what you'd think: Make sense of digital writing. The toolkit both understands words and provides features typically found in text editors, like alignment and bulleting. While Project Ink Analysis is still in its experimental stages, it could obviously help anyone who habitually writes with styluses on digital platforms. It might not garner deep insights into your personality like IBM Watson, but its simple beautification tools can clean up chickenscratch and even translate from 67 languages. It could be plenty useful for all the Surface Pen users out there who want their scrawling handwriting to look just a bit more professional (and legible).

Audi Gets Handwriting Recognition Right in the 2019 A8


The 2019 Audi A8's handwriting recognition feature lets you find coffee quickly by writing Starbucks on the screen.

Windows 10 digital ink: All the improvements with the Fall Creators Update


Inking and navigating with a digital pen or stylus within Windows 10 will become easier within the Fall Creators Update, for those of you who use a tablet as, you know, a tablet. The improvements include two major elements: navigation, including using the pen or stylus to select and scroll text; and better interpretation of inked words as text, via a more accurate and responsive handwriting panel. Combined, it's a love letter of sorts to Surface and other tablet users who use the pen to input data. It's amazing how well Windows can interpret your chicken-scratch into text that can be edited in Word and elsewhere. General Windows 10 users won't be able to take advantage of the new features until the launch of the Fall Creators Update on Oct. 17.

AllAnalytics - James M. Connolly - Handwriting Recognition Meets Machine Learning


There are places in the tech space where we cease to stare in amazement about what the tech can do. Instead we whine that the tech can't do more. Take the case of handwriting recognition, whether it's what we scribble notes onto a tablet or when we scan handwritten text into a PC. We wish that it was smarter, that it recognized more characters and that the text was searchable and shareable. To be honest, I shouldn't say "we".

How 'cognitive ergonomics' will humanise AI technology Information Age


Whether exchanging dialogue with our smartphones or scribbling characters on touchscreens, the Human-Machine Interfaces (HMI) we interact with today are intuitive and foster'easy to use' input methods. Driven by speech, handwriting and touch, our technologies are continually progressing towards intuitive communication between humans and machines, and we are continuing to march forward. However, several advancements in artificial intelligence technology, such as machine and deep learning capabilities, have paved the way for the humanistion of our machines and devices. And there's one particular development in the AI space which has pioneered the ability for seamless human-to-machine interaction - cognitive ergonomics. Through cognitive ergonomics, system designs that allows machines to adapt and operate considering mental workloads and other factors, we are able to communicate with our devices as easy as writing a note on paper.

Identification of arabic word from bilingual text using character features

arXiv.org Artificial Intelligence

The identification of the language of the script is an important stage in the process of recognition of the writing. There are several works in this research area, which treat various languages. Most of the used methods are global or statistical. In this present paper, we study the possibility of using the features of scripts to identify the language. The identification of the language of the script by characteristics returns the identification in the case of multilingual documents less difficult. We present by this work, a study on the possibility of using the structural features to identify the Arabic language from an Arabic / Latin text.

Context-Bounded Refinement Filter Algorithm: Improving Recognizer Accuracy of Handwriting in Clock Drawing Test

AAAI Conferences

Early detection of cognitive impairment can prevent or delay the progress of cognitive dysfunction. In the field of neurology, the Clock Drawing Test (CDT) is one of the most popular instruments for detecting cognitive impairment. This paper presents the development of the ClockReader system, a computerized Clock Drawing Test. The main function of the system is to automate error handling in handwriting recognition. Since the ClockReader is a screening tool for dementia, it is not desirable to ask the users to fix their input errors in the drawing of either numbers or characters. Therefore, we propose a simple machine learning technique, context-bounded refinement filter algorithm. With trial experiments, we prove that this simple algorithm improves the recognizer accuracy of handwriting in clock drawings up to 88%.

Using Entropy to Identify Shape and Text in Hand Drawn Diagrams

AAAI Conferences

Most sketch recognition systems are accurate in recognizing either text or shape (graphic) ink strokes, but not both. Distinguishing between shape and text strokes is, therefore, a critical task in recognizing hand drawn digital ink diagrams which commonly contain many text labels and annotations. We have found the ‘entropy rate’ to be an accurate criterion of classification. We found that the entropy rate is significantly higher for text strokes compared to shape strokes and can serve as a distinguishing factor between the two. Using entropy values, our system produced a correct classification rate of 92.06% on test data belonging to diagrammatic domain for which the threshold was trained on.  It also performed favorably on data for which no training examples at all were supplied.