We are talking here Optical character recognition Technology as part of Document Management functions of leadorganizer.net
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.
For more complex recognition problems, intelligent character recognition systems are generally used, as artificial neural networks can be made indifferent to both affine and non-linear transformations.
ref:Insurance Document Management Software, wikipedia
Thursday, December 27, 2007
Monday, December 24, 2007
OCR Technology
We are talking Document Management System here and as a part of DMS, we talked about OCR in our last post. Today we are going to talk more about OCR Technology.
Current Status of OCR Technology:
The accurate recognition of Latin-script, typewritten text is now considered largely a solved problem. Typical accuracy rates exceed 99%, although certain applications demanding even higher accuracy require human review for errors. Handwriting recognition, including recognition of hand printing, cursive handwriting, is still the subject of active research, as is recognition of printed text in other scripts (especially those with a very large number of characters)
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 applications. This variety of OCR is now commonly known in the industry as ICR, or Intelligent Character Recognition.
Current Status of OCR Technology:
The accurate recognition of Latin-script, typewritten text is now considered largely a solved problem. Typical accuracy rates exceed 99%, although certain applications demanding even higher accuracy require human review for errors. Handwriting recognition, including recognition of hand printing, cursive handwriting, is still the subject of active research, as is recognition of printed text in other scripts (especially those with a very large number of characters)
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 applications. This variety of OCR is now commonly known in the industry as ICR, or Intelligent Character Recognition.
we continue our talk on OCR Technology in next post.
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