The computer calligraphy is same as human
Computer calligraphy. For some, the personal touch that comes from a handwritten letter is being lost due to the use of email, text messages and computers to send messages. But computers themselves may soon have their own personalised handwriting with the help of networks that mimic the structure of the human brain.
Computer scientists have created a system known as a ‘recurrent neural network’ that can generate its own handwriting. The program can learn how to create handwriting-like text by examining the movement of a pen as real people write. The system can then use this to mimic the handwriting styles and even create some of it is own.
While the system still needs to have text inputted – it cannot yet produce its own content – it can produce authentic looking handwritten notes.
IS HANDWRITING A DYING ART?
From next year, children in Finland will not be compulsorily taught cursive handwriting. Instead of learning this skill, schools will be given the choice to teach typing in its place. The country’s education board said the change reflects how typing skills are now more relevant than handwriting, but experts claim the move could damage a child’s brain development.
It follows changes made to the Common Core Standards Initiative in the US, in September 2013, in which the US similarly removed cursive handwriting as a compulsory skill. In a recent study from Indiana University, researchers conducted brain scans on five-year-olds before and after receiving different letter-learning tasks. In children who practiced writing letters by hand, the neural activity was more enhanced and ‘adult-like’ than in those who had simply looked at letters.
And, the brain’s so-called ‘reading circuit’ – a region of linked connections that become active when reading – was activated during handwriting, but not during typing. MailOnline tested it with lyrics from Taylor Swift’s Bad Blood and an extract from the famous Gettysburg Address given by Abraham Lincoln in 1863. The results look more like the efforts of a primary school child than the carefully crafted letters of a calligraphy artist.
Some of the words run into each other, others are illegible and the lines themselves can slant alarmingly downhill. At one point the system also appeared to suffer some something many students will likely identify with – the virtual pen seemed to drift off the page as if it had fallen asleep mid-flow. It will even scribble out mispelt words and errors, much like a real human.
The system was developed by computer scientist Alex Graves, a computer scientist at the University of Toronto to demonstrate how a neural network can be trained. He said:
‘The resulting sequences are sufficiently convincing that they often cannot be distinguished from real handwriting. Furthermore, this realism is achieved without sacrificing the diversity in writing style.’
The computer can generate one of five styles that mimic the writing style of a human or it can create an entirely random style of its own.
An online prototype of the system allows users to input 100 characters at a time, meaning longer pieces of text can often look slightly variable.
However, it is possible to make the handwriting more legible and neater using a ‘bias’ slider that improves the handwriting.
Recurrent neural networks are being developed as part of artificial intelligence research by mimicking the connections of neurons by connecting each ‘unit’ in it to every other unit. It connects the basic units in a cycle which allows it to deal with ‘fuzzy’ information like handwriting.
Mr Graves, who has published a paper on his work on arXiv.org, said he hoped to adapt the handwriting system to more realistic voice generation too. He said:
‘(It) which is likely to be more challenging than handwriting synthesis due to the greater dimensionality of the data points. It would also be interesting to develop a mechanism to automatically extract high-level annotations from sequence data. In the case of handwriting, this could allow for more nuanced annotations than just text, for example stylistic features, different forms of the same letter, information about stroke order and so on.’