The Japanese writing system is complex, with three character types of Hiragana, Katakana, and Kanji. Kanji consists of thousands of unique characters, further adding to the complexity of character identification and literature understanding. Being able to translate handwritten Japanese characters into digital text is useful for data analysis, translation, learning and cultural preservation. In this study, a machine learning approach to analyzing and recognizing handwritten Japanese characters (Kanji) is proposed. The study used an ensemble of three convolutional neural networks (CNNs) for recognizing handwritten Kanji characters and utilized four datasets of MNIST, K-MNIST, Kuzushiji-49 (K49) and the top 150 represented classes in the Kuzushiji-Kanji (K-Kanji) dataset for its performance evaluation. The results indicate feasibility of using proposed CNN-ensemble architecture for recognizing handwritten characters, achieving 99.4%, 96.4%, 95.0% and 96.4% classification accuracy on MNIST, K-MNIS, K49, and K-Kanji datasets respectively.

The physical autonomy of robots is well understood both theoretically and practically. By contrast, there is almost no research exploring their potential economic autonomy. In this paper, we present the first economically autonomous robot -- a robot able to produce marketable goods while having full control over the use of its generated income. Gaka-chu ("painter" in Japanese) is a 6-axis robot arm that creates paintings of Japanese characters from an autoselected keyword. By using a blockchain-based smart contract, Gaka-chu can autonomously list a painting it made for sale in an online auction. In this transaction, the robot interacts with the human bidders as a peer not as a tool. Using the blockchain-based smart contract, Gaka-chu can then use its income from selling paintings to replenish its resources by autonomously ordering materials from an online art shop. We built the Gaka-chu prototype with an Ethereum-based smart contract and ran a 6-month long experiment, during which the robot created and sold four paintings, simultaneously using its income to purchase supplies and repay initial investors. In this work, we present the results of the experiments conducted and discuss the implications of economically autonomous robots.

The everyday tasks that humans often take for granted, such as walking and handwriting, have, until recently, been challenging to program a robot to do. The Handwriting Company now has a robot that can create beautifully handwritten communication that mimics the style of an individual's handwriting while a robot from Brown University can replicate handwriting from a variety of languages even though it was just trained on Japanese characters. Achieving this milestone of robotic capabilities was quite a feat for the researchers and roboticists behind the scenes and the machine learning algorithms that power the robots' skills. Let's review what's currently possible today and what might be in store for the future with robots creating handwritten text. Hemingway is The Handwriting Company's robot that can mimic anyone's style of handwriting.

Among the many things we humans like to lord over the rest of the animal kingdom is our complex language. Sure, other creatures talk to one another, but we've got all these wildly complicated written languages with syntax and fun words like defenestrate. This we can also lord over robots, who, in addition to lacking emotion and the ability to not fall on their faces, can't write novels. Researchers at Brown University just got a robot to do something as linguistically improbable as it is beautiful: After training to hand-write Japanese characters, the robot then turned around and started to copy words in a slew of other languages it'd never written before, including Hindi, Greek, and English, just by looking at examples of that handwriting. Not only that, it could do English in print and cursive.

One day in the not-so-distant future, robots could help humans out in the workplace by taking notes or sketching helpful diagrams. That's one of the objectives of a new robot created by researchers from Brown University that can learn to write languages and sketch drawings practically on its own. After learning to write Japanese characters, the robot was able to teach itself how to copy words in 10 different languages, including Hindi, Greek and English, just by studying various examples. It uses an algorithm that helps the robot decide where and how to place each pen stroke that distinguishes each letter in the alphabet, as well as what order to place them in to make the correct word. 'Just by looking at a target image of a word or sketch, the robot can reproduce each stroke as one continuous action,' Atsunobi Kotani, who led the study, said in a statement.

You are free to share this article under the Attribution 4.0 International license. A new algorithm enables robots to put pen to paper, writing words using stroke patterns similar to human handwriting. It's a step, the researchers say, toward robots that are able to communicate more fluently with human coworkers and collaborators. "Just by looking at a target image of a word or sketch, the robot can reproduce each stroke as one continuous action," says Atsunobu Kotani, an undergraduate student at Brown University who led the algorithm's development. "That makes it hard for people to distinguish if it was written by the robot or actually written by a human."

This article originally appeared in Vulture. When the first trailer arrived for Isle of Dogs last fall, I had three immediate, consecutive reactions: One: Oh, no. Two: Wait, I take that back. I'm going to be a good critic and reserve judgement until the week of March 23. In the week since Isle of Dogs' initial limited release, a measured, varied, and nuanced discussion about Wes Anderson's use of Japanese culture--and other cultures in general--has happened in fits and starts.

Jiro's pick this week is CNN for Old Japanese Character Classification by one of my colleagues Akira Agata. Nowadays, I probably go many days without seeing a handwritten document. From computers, to smartphones, to TVs, to books, almost every character I see is a printed character. So it's refreshing to see a handwritten document from time to time. This demo by Akira uses deep learning (convolutional neural networks) to classify various handwritten Japanese characters.