%0 Journal Article %1 noauthororeditor %A Narendra Sahu, Manoj Sonkusare %D 2017 %J A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUES %K Character Identification Optical %N 1 %P 14 %R 10.5121/ijcsitce.2017.4101 %T A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUES %U http://airccse.com/ijcsitce/papers/4117ijcsitce01.pdf %V 4 %X Optical Character Recognition (OCR) is the process which enables a system to without human intervention identifies the scripts or alphabets written into the users’ verbal communication. Optical Character identification has grown to be individual of the mainly flourishing applications of knowledge in the field of pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this paper we resolve and examine the hypothetical and numerical models of Optical Character Identification. The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR) techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on Optical Character detection or recognition has shown that the In Handwritten text there is no limitation lying on the script technique. Hand written correspondence is complicated to be familiar through due to diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of approaches of Optical Character Identification is discussed here all along through their achievement.

@article{noauthororeditor, abstract = {Optical Character Recognition (OCR) is the process which enables a system to without human intervention identifies the scripts or alphabets written into the users’ verbal communication. Optical Character identification has grown to be individual of the mainly flourishing applications of knowledge in the field of pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this paper we resolve and examine the hypothetical and numerical models of Optical Character Identification. The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR) techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on Optical Character detection or recognition has shown that the In Handwritten text there is no limitation lying on the script technique. Hand written correspondence is complicated to be familiar through due to diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of approaches of Optical Character Identification is discussed here all along through their achievement. }, added-at = {2017-11-01T11:32:00.000+0100}, author = {Narendra Sahu, Manoj Sonkusare}, biburl = {https://www.bibsonomy.org/bibtex/28362af0e12bc8696c795ad5bb9969927/suzan}, doi = {10.5121/ijcsitce.2017.4101}, interhash = {657393996f8bd1c5471729d0d24c275d}, intrahash = {8362af0e12bc8696c795ad5bb9969927}, journal = {A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUES}, keywords = {Character Identification Optical}, month = {January}, number = 1, pages = 14, timestamp = {2017-11-01T11:32:00.000+0100}, title = {A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUES}, url = {http://airccse.com/ijcsitce/papers/4117ijcsitce01.pdf}, volume = 4, year = 2017 }