Handwriting recognition
Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices.[1][2] The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition) or intelligent word 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.
This article is about recognizing the specific letters and words in hand-written text. For recognizing the specific person who wrote hand-written text, see Graphanalysis.
Handwriting recognition has an active community of academics studying it. The biggest conferences for handwriting recognition are the International Conference on Frontiers in Handwriting Recognition (ICFHR), held in even-numbered years, and the International Conference on Document Analysis and Recognition (ICDAR), held in odd-numbered years. Both of these conferences are endorsed by the IEEE and IAPR.
In 2021, the ICDAR proceedings will be published by LNCS, Springer.
Active areas of research include:
Results since 2009[edit]
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.[13] In particular, the bi-directional and multi-dimensional Long short-term memory (LSTM)[14][15] 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[16] on the famous MNIST handwritten digits problem[17] of Yann LeCun and colleagues at NYU.
Benjamin Graham of the University of Warwick won a 2013 Chinese handwriting recognition contest, with only a 2.61% error rate, by using an approach to convolutional neural networks that evolved (by 2017) into "sparse convolutional neural networks".[18][19]