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Handwriting Recognition Software Reference – The Billion Dollar Site
Reference Section @ BillDoll.com – The Billion Dollar Site
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Handwriting Recognition Software
This section provides web resources for the various aspects of handwriting recognition software & technology.
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Content derived from Wikipedia article on Handwriting Recognition
Handwriting recognition
Handwriting recognition is the ability of a computer to receive intelligible handwritten input. The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition). Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface.
Handwriting recognition principally entails optical character recognition. However, a complete handwriting recognition system also handles formatting, performs correct segmentation into characters and finds the most plausible words.
On-line recognition
On-line 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 X(t),Y(t) as well as pen-up/pen-down switching. That kind of data is known as digital ink and can be regarded as a dynamic representation of handwriting. The obtained signal is converted into letter codes which are usable within computer and text-processing applications.
The elements of an on-line handwriting recognition interface typically include:
Handwriting recognition is commonly used as an input method for PDAs. 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. By the time of the release of the Newton OS 2.0, wherein the handwriting recognition was greatly improved, including unique features still not found in current recognition systems such as modeless error correction, the largely negative first impression had been made. Another effort was Go's tablet computer using Go's Penpoint operating system and manufactured by various hardware makers such as NCR and IBM. IBM's Thinkpad tablet computer was based on Penpoint operating system and used IBM's handwriting recognition. This recognition system was later ported to Microsoft Windows for Pen, and IBM's Pen for OS/2. None of these were commercially successful.
Palm later launched a successful series of PDAs based on the Graffiti® recognition system. Graffiti improved usability by defining a set of pen strokes for each character. This narrowed the possibility for erroneous input, although memorization of the stroke patterns did increase the learning curve for the user.
A modern handwriting recognition system can be seen in Microsoft's version of Windows XP operating system for Tablet PCs. A Tablet PC is a special notebook computer that is outfitted with a digitizer tablet and a stylus, and allows a user to handwrite text on the unit's screen. The operating system recognizes the handwriting and converts it into typewritten text. Notably, Microsoft's system does not attempt to learn a user's writing pattern and instead maintains an internal recognition database containing thousands of possible letter shapes. This system is distinct from the less advanced handwriting recognition system employed in its Windows Mobile OS for PDAs.
In recent years, several attempts were made to produce ink pens that include digital elements, such that a person could write on paper, and have the resulting text stored digitally. The success of these products is yet to be determined.
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. It is still generally accepted that keyboard input is both faster and more reliable. On PDAs, the Graffiti system is being phased out in favor of keyboards.
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Off-line recognition
Off-line handwriting recognition involves the automatic conversion of text in an image I(x,y) into letter codes which are usable within computer and text-processing applications. The data obtained by this form is regarded as a static representation of handwriting.
The technology is successfully used by businesses which process lots of handwritten documents, like insurance companies. The quality of recognition can be substantially increased by structuring the document (by using forms).
Related topics at Wikipedia:
End of Wikipedia content, http://en.wikipedia.org/wiki/Handwriting_recognition
Content derived from Wikipedia article on Optical Character Recognition
Optical character recognition
Optical character recognition, usually abbreviated to OCR, is computer software designed to translate images of handwritten or typewritten text (usually captured by a scanner) into machine-editable text, or to translate pictures of characters into a standard encoding scheme representing them (e.g. ASCII or Unicode). OCR began as a field of research in pattern recognition, artificial intelligence and machine vision. Though academic research in the field continues, the focus on OCR has shifted to implementation of proven techniques.
Optical character recognition (using optical techniques such as mirrors and lenses) and digital character recognition (using scanners and computer algorithms) were originally considered separate fields. Because very few applications survive that use true optical techniques, the optical character recognition term has now been broadened to cover digital character recognition as well.
Early systems required "training" (essentially, the provision of known samples of each character) to read a specific font. Currently, though, "intelligent" systems that can recognize most fonts with a high degree of accuracy are now common. Some systems are even capable of reproducing formatted output that closely approximates the original scanned page including images, columns and other non-textual components.
History
In 1929, G. Tauschek obtained a patent on OCR in Germany, followed by Handel who obtained a US patent on OCR in USA in 1933 (U.S. Patent 1,915,993). Tauschek was in 1935 also granted a US patent on his method (U.S. Patent 2,026,329).
Tauschek's machine was a mechanical device that used templates. A photodetector was placed so that when the template and the character to be recognised was lined up for an exact match, and a light was directed towards it, no light would reach the photodetector.
In 1950, David Shepard, a cryptanalyst at the Armed Forces Security Agency in the United States, was asked by Frank Rowlett, who had broken the Japanese PURPLE diplomatic code, to work with Dr. Louis Tordella to recommend data automation procedures for the Agency. This included the problem of converting printed messages into machine language for computer processing. Shepard decided it must be possible to build a machine to do this, and, with the help of Harvey Cook, a friend, built "Gismo" in his attic during evenings and weekends. This was reported in the Washington Daily News on April 27, 1951 and in the New York Times on December 26, 1953 after his U.S. Patent Number 2,663,758 was issued. Shepard then founded Intelligent Machines Research Corporation (IMR), which went on to deliver the world's first several OCR systems used in commercial operation. While both Gismo and the later IMR systems used image analysis, as opposed to character matching, and could accept some font variation, Gismo was limited to reasonably close vertical registration, whereas the following commercial IMR scanners analyzed characters anywhere in the scanned field, a practical necessity on real world documents.
The first commercial system was installed at the Readers Digest in 1955, which, many years later, was donated by Readers Digest to the Smithsonian, where it was put on display. The second system was sold to the Standard Oil Company of California for reading credit card imprints for billing purposes, with many more systems sold to other oil companies. Other systems sold by IMR during the late 1950s included a bill stub reader to the Ohio Bell Telephone Company and a page scanner to the United States Air Force for reading and transmitting by teletype typewritten messages. IBM and others were later licensed on Shepard's OCR patents.
The United States Postal Service has been using OCR machines to sort mail since 1965 based on technology devised primarily by the prolific inventor Jacob Rabinow. The first use of OCR in Europe was by the British General Post Office or GPO. In 1965 it began planning an entire banking system, the National Giro, using OCR technology, a process that revolutionized bill payment systems in the UK. Canada Post has been using OCR systems since 1971. OCR systems read the name and address of the addressee at the first mechanized sorting center, and print a routing bar code on the envelope based on the postal code. After that the letters need only be sorted at later centers by less expensive sorters which need only read the bar code. To avoid interference with the human-readable address field which can be located anywhere on the letter, special ink is used that is clearly visible under ultraviolet light. This ink looks orange in normal lighting conditions. Envelopes marked with the machine readable bar code may then be processed.
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Current state of OCR Technology
Typewritten OCR
The accurate recognition of Latin-script, typewritten text is now considered largely a solved problem.
Recognition of hand printing, cursive handwriting, and even the printed typewritten versions of some other scripts (especially those with a very large number of characters), are still the subject of active research.
Hand print OCR
Systems for recognizing hand-printed text on the fly have enjoyed commercial success in recent years. Among these are the input device for personal digital assistants such as those running Palm OS. The Apple Newton pioneered this technology. The algorithms used in these devices take advantage of the fact that the order, speed, and direction of individual lines segments at input are known. Also, the user can be retrained to use only specific letter shapes. These methods cannot be used in software that scans paper documents, so accurate recognition of hand-printed documents is still largely an open problem. Accuracy rates of 80% to 90% on neat, clean hand-printed characters can be achieved, but that accuracy rate still translates to dozens of errors per page, making the technology useful only in very limited contexts. This variety of OCR is now commonly known in the industry as "ICR" (intelligent character recognition).
Cursive OCR
Recognition of cursive text is an active area of research, with recognition rates even lower than that of hand-printed text. Higher rates of recognition of general cursive script will likely not be possible without the use of contextual or grammatical information. For example, recognizing entire words from a dictionary is easier than trying to parse individual characters from script. Reading the Amount line of a cheque (which is always a written out number) is an example where using a smaller dictionary can increase recognition rates greatly. Knowledge of the grammar of the language being scanned can also help determine if a word is likely to be a verb or a noun, for example, allowing greater accuracy. The shapes of individual cursive characters themselves simply do not contain enough information to accurately (greater than 98%) recognize all handwritten cursive script.
Music OCR
Early research into recognition of printed sheet music was performed at the graduate level in the mid 1970's at MIT and other institutions. Successive efforts were made to localize and remove musical staff lines leaving symbols to be recognized and parsed. The first commercial music-scanning product, MIDISCAN, was released in 1991. Several commercial products are now available.
MICR
One area where accuracy and speed of computer input of character information exceeds that of humans is in the area of magnetic ink character recognition, where the error rates range around one read error for every 20,000 to 30,000 checks.
Other research areas
A particularly difficult problem for computers and humans is that of old church baptismal and marriage records containing mostly names. The pages may be damaged by age, water or fire and the names may be obsolete or contain rare spellings. Another research area is cooperative approaches, where computers assist humans and vice-versa. Computer image processing techniques can assist humans in reading extremely difficult texts such as the Archimedes Palimpsest or the Dead Sea Scrolls.
Generally, for more complex recognition problems neural networks are commonly used as they generally can be made indifferent to both affine and non-linear transformations.
A related area is raster to vector conversion, converting bitmap images (for example, maps including drawings, text, and map symbols) into vector graphics that are easier to work with.
Related Topics in Wikipedia
External links
ICDAR ICDAR is one of the most comprehensive conferences on all aspects of document recognition, including OCR, and is held every two years. phpOCR A base implementation for an OCR tool in PHP GNU Ocrad "is an OCR [...] program based on a feature extraction method". DRR SPIE DRR is an annual conference on OCR and document retrieval. Reference OCR Engine An open-source OCR project. OOCR OOCR is an OCR program still in development, under the GPL. GOCR GOCR is an OCR program, developed under the GPL. Tesseract Tesseract is an open source OCR, initially developed by HP, and released under the Apache License, Version 2.0. It can be compiled using MSVC 6.0 or GCC.
End of Wikipedia content, http://en.wikipedia.org/wiki/Optical_character_recognition
Content derived from Wikipedia article on Scriptnetics
Scriptnetics is the study of manual organic data interaction for communication or control, often involving regulatory feedback, in living organisms and in machines and their combinations. The term derives its root from cybernetics and the Latin word scriptor (writer, author or scribe) to describe scriptnetics as a subset of cybernetics that relates to the method of data input for communication or control and governance. It is a recent term that is often considered without being named in subject matters that are specializations of cybernetics under the headings of adaptive systems, artificial intelligence, complex systems, complexity theory, control systems, decision support systems, dynamical systems, information theory, learning organizations, mathematical systems theory, operations research, simulation, and systems engineering.
A more philosophical definition follows from the 1956 cybernetics definition of Louis Couffignal, one of the pioneers of cybernetics, as "the art of ensuring the efficiency of action” to one for scriptnetics as “interaction for the art of ensuring the efficiency of action."
History and Origins
Scriptnetics is related to the movement begun in the early 1970's of second-order cybernetics that distinguished themselves from the control engineering and computer science disciplines who had become fully independent within cybernetics. The remaining cybneticists felt they needed to clearly distinguish themselves by emphasizing autonomy, self-organization, cognition and the role of the observer in the modeling system. Some controversy surrounds the second-order movement because of the emphasis on the irreducible complexity of the various system-observer interactions and its ensuing subjectiveness. Second-order cybernetics may continue to be concerned with less objective and less mathematical approaches.
The development of computer sound recognition and computer graphics systems, particularly since the beginning of the millenium, including the advancement of voice and handwriting, allowed an emphasis on organic interaction to and from machines with the observer having a greater influence over the system without actually being part of the system. At the same time the observer is influenced down to the subconscious level by feedback from the machine. For example, handwriting to text predictive computer systems make the user inputing handwriting "think" the computer system is "learning" to interpret the handwriting of the user. In reality, the user is learning subconsciously through the feedback of immediate results to better form the handwriting graphics. Scriptnetics is the study and application of this recently observed interaction phenomena using empirical observations and tests.
Scope
In scholarly terms, Scriptnetics is the study of input systems or control in an abstracted sense — that is, it is not grounded in any one system but can be measured empirically to test results.
The emphasis is on the parts of a system in the functional relations that hold between the different parts of a system. These relations include the transfer of information, and circular relations (feedback) that result in emergent phenomena such as self-organization, and, (expressed as a term coined by Humberto Maturana, Francisco Varela and Ricardo Uribe), autopoiesis. The main innovation of scriptnetics is the creation of a scientific discipline focused on results: an understanding of results from a negative feedback loop which minimizes the deviation between the perceived situation and the desired results.
In practical terms, scriptnetics is a re-introduction of the role of the observer as a player to establish a sub-definition of cybernetics in the control engineering and computer science disciplines rather than defining the importance of autonomy, self-organization, cognition and the role of the observer as a new broadly based disciple, second-order cybernetics. The emphasis in scriptnetics is the empirical measurement of events to determine a model that will lead to predictable results.
Examples of scriptnetics to describe the process of organic input of data into a mechanical control or computer system for interaction with that system; a computer keyboard, voice activation in phone systems, handwriting recognition or drawing on electronic pads.
References
Heylighen F., and Joslyn C. (2001), “Cybernetics and Second Order Cybernetics”, in: R.A. Meyers (ed.), Encyclopedia of Physical Science & Technology (3rd ed.), Vol. 4, (Academic Press, New York), p. 155-170.
Pangaro, Paul (1990), “Cybernetics — A Definition”, Eprint.
Von Foerster, Heinz (1995), “Ethics and Second-Order Cybernetics”, Eprint.
End of Wikipedia content, http://en.wikipedia.org/wiki/Scriptnetics
Content derived from Wikipedia article on Optical Character Recognition Software Products
Optical character recognition software, OCR software, is used for the recognition of writing and printing. OCR software is still relatively new, but with increases in computer power and recognition software the accuracy is improving.
Proprietary software
Free and open source OCR software
End of Wikipedia content, http://en.wikipedia.org/wiki/Optical_character_recognition_software
More topics to be discussed
Credits & Copyright: This page is licensed under the GNU Free Documentation License. It uses material from the Wikipedia articles Handwriting recognition, Optical character recognition, Scriptnetics, Optical character recognition software
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