Even though OCRs were useful in interpreting printed text, the recognition of handwritten text was not as accurate.Thus, the ICR handwritten recognition was developed based on a system of Artificial Intelligence known as Neural Network Technology.
The neural network regularly updates its handwritten pattern database to yield increasingly efficient results. The success of ICR handwritten recognition has increased the potential of scanning devices by enabling the extraction and storage of valuable data from structured handwritten documents. However, the accuracy varies with different handwriting styles and patterns, though a high percentage of accuracy of above 97 can be expected. Script writing (disjoint characters) is found to be more easily recognizable than cursive forms of writing. These patterns are compared to the images stored in the neural network database. The interpretation or translated result is put forth on the basis of a majority vote by the several read engines employed for the benefit of increasing the accuracy of results. For handwritten text, the votes of engines meant to interpret handwritten data are given priority while for numerical data, the votes of engines employed to interpret numerical text are given priority. Icr Software For Handwritten Documents Portable Devices ThatICR handwriting recognition technology is now being utilized in electronic and portable devices that use touch screens and touch pads of device is like mobile phones and planners where the sensors automatically generate interpretation of handwritten text onto the devices screen. However, while entering data into a computer, this method is found to be less efficient than data input through a keyboard. CVISION, CVista, CBatch, and the CVISION logo are registered trademarks of CVISION Technologies, Inc.
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