Handwriting recognition is used most often to describe the ability of a computer to translate human writing into text. This may take place in one of two ways, either by scanning of written text or by writing directly on to a peripheral input device.
The first of these handwriting recognition techniques, known as optical character recognition (OCR), is the most successful in the mainstream. Most scanning suites offer some form of OCR, allowing users to scan in handwritten documents and have them translated into basic text documents. OCR is also used by some archivists as a method of converting massive quantities of handwritten historical documents into searchable, easily-accessible digital forms.
The second group of handwriting recognition techniques, often referred to as on-line recognition, has experienced an ebb and flow in popularity. In the 1990s, Apple Computers released a handheld device called the Newton which made use of the first widely available handwriting recognition interface. By using a small stylus, the user was able to write directly on the Newton's screen and (in theory) have their letters recognized and converted to text. In practice, the software the Newton used to attempt to learn user handwriting patterns was less than ideal, and as a result its popularity was never great.
Later, the Palm company tried a new handwriting recognition system, which they called Graffiti. Rather than relying on an intuitive use of the traditional Roman alphabet, the Graffiti system defined its own system of much simpler line-strokes as stand-ins for each letter. This allowed it a higher success rate in identifying letters and learning a user's variations, but made for a steep learning curve which kept most mainstream users at bay.
Microsoft Corporation's Tablet PCs also make use of a handwriting recognition system. Rather than attempt to learn a user's nuances, however, the Tablet PCs draw on an extensive database of character variation. This system appears to have a higher success rate for most users than do adaptive systems, but also seems to have a threshold to its reliability.
Research on handwriting recognition software has begun picking up speed again, with the mainstreaming of PDAs and cellular phones with stylus inputs. What was once the realm of fringe technologists is rapidly becoming a multi-billion dollar market, prompting many companies to restart their investigations into handwriting recognition.
While the problems impeding the creation of a strong, reliable handwriting recognition system are great, recent breakthroughs indicate that it is only a matter of time before near-perfect recognition becomes a reality for the mainstream.
