Handwriting recognition
Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface, a generally easier task as there are more clues available.
A handwriting recognition system handles formatting, performs correct segmentation into characters, and finds the most possible words.
Offline handwriting recognition involves the automatic conversion of text in an image into letter codes that are usable within computer and text-processing applications.
Yet any system using this approach requires substantially more development time than a neural network because the properties are not learned automatically.
[4] Online handwriting recognition involves the automatic conversion of text as it is written on a special digitizer or PDA, where a sensor picks up the pen-tip movements as well as pen-up/pen-down switching.
Commercial products incorporating handwriting recognition as a replacement for keyboard input were introduced in the early 1980s.
The first PDA to provide written input was the Apple Newton, which exposed the public to the advantage of a streamlined user interface.
However, the device was not a commercial success, owing to the unreliability of the software, which tried to learn a user's writing patterns.
This narrowed the possibility for erroneous input, although memorization of the stroke patterns did increase the learning curve for the user.
The features include a "personalization wizard" that prompts for samples of a user's handwriting and uses them to retrain the system for higher accuracy recognition.
Although handwriting recognition is an input form that the public has become accustomed to, it has not achieved widespread use in either desktop computers or laptops.
In the early 1990s, two companies – ParaGraph International and Lexicus – came up with systems that could understand cursive handwriting recognition.
Microsoft has acquired CalliGrapher handwriting recognition and other digital ink technologies developed by P&I from Vadem in 1999.
Active areas of research include: Since 2009, the recurrent neural networks and deep feedforward neural networks developed in the research group of Jürgen Schmidhuber at the Swiss AI Lab IDSIA have won several international handwriting competitions.
[15] In particular, the bi-directional and multi-dimensional Long short-term memory (LSTM)[16][17] of Alex Graves et al. won three competitions in connected handwriting recognition at the 2009 International Conference on Document Analysis and Recognition (ICDAR), without any prior knowledge about the three different languages (French, Arabic, Persian) to be learned.
Recent GPU-based deep learning methods for feedforward networks by Dan Ciresan and colleagues at IDSIA won the ICDAR 2011 offline Chinese handwriting recognition contest; their neural networks also were the first artificial pattern recognizers to achieve human-competitive performance[18] on the famous MNIST handwritten digits problem[19] of Yann LeCun and colleagues at NYU.
