OCR, ICR and OMR are very closely related technologies, rather than one leading to the other. OCR concentrates more on recognizing scanned images- generally machine prints. Using OCR (Optical character recognition) technology, we can translate image documents into text searchable files, which give us the advantage of lesser storage space, user-desired format storage, and more portability. The technology takes note of the text font, dark and light areas, grammar, language, and the background. OCR, ICR and OMR underlying technologies are very similar. OCR uses a variety of algorithms, multiple neural network technology, polling the result of the many algorithms, to finalize on one resulting translated text file. The OCR technology converts the input file into a machine-code, one that the computer can understand, and finally relates the codes to predefined matches of the particular code, and a character. By converting the file type, we get to edit the text, and manipulate it according to our wish. OCR however, is still more restricted to machine prints. As technology improvised and got ambitious, the need for hand-written text recognition got more serious and hence ICR was born.
ICR (Intelligent character recognition) technology is an extension of the OCR, and is more complex when compared to OCR, ICR and OMR, the simple reason being that, it is tougher to recognize human handwritten text than machine derivatives. Again, here the scanned text is reviewed and translated into machine codes like ASCII, and then co related with characters. An element of intelligence is involved in ICR, as ICR tends to think like a human when there are ambiguities and unclear segments in the text. This means that the ICR has to have an inbuilt dictionary, grammar comprehension to decide an unsure part of the text recognition process. Thus, ICR works with a human approach to the translation process of a handwritten script and is more robust when compared to OCR.
OMR (Optical Mark recognition) is extensively used in examinations, companies, for computing scores, data, based on marks made on paper. It is more ancient when compared to OCR, ICR and OMR. It is a quick process, and hence is adopted by institutions for calibrating scores of their employees or students. Apart from being speedy and accurate, it is an easy process with no typing errors, misconceptions involved. However, OMR machines are highly selective and particular about the condition of the paper fed in, neatness, and so on. Hence programmers can load OMR software technologies for quick assessing of questionnaires, exams and other reports that involve marking. OCR, ICR and OMR are linked to each other with a thread of recognizing an input, to bring out a useful output.