Copyright © 2018K. Karthick et. al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. International Journal of Engineering & Technology, 7 (4) (2018) 2895-2898 International Journal of Engineering & Technology Website: www.sciencepubco.com/index.php/IJET doi: 10.14419/ijet.v7i4.15311 Research paper Study on diverse automatic identification techniques K.Karthick 1 *, M.Premkumar 2 , R.Manikandan 3 , R.Cristin 4 1 Associate Professor, Department of EEE, GMR Institute of Technology, Rajam 2 Assistant Professor, Department of EEE, GMR Institute of Technology, Rajam 3 Professor, Department of EIE, Panimalar Engineering College, Chennai 4 Sr. Assistant Professor, Department of CSE, GMR Institute of Technology, Rajam *Corresponding author E-mail:kkarthiks@gmail.com Abstract Digital Image Processing system is becoming popular due to easy availability of personal computers, the large size of memory devices, graphics software, etc. Image Processing is involved in various applications such as film industry, remote sensing, non-destructive evalu- ation, forensic studies, medical imaging, textiles, material science, military, document processing, graphic arts and printing industry. Feeding the data into the computer using the keyboard is a traditional way. The automatic identification is an alternate solution for manu- al entry of data. In this paper, we discussed the various techniques involved in automatic identification without / partial human interac- tion. Keywords:Automatic Identification Techniques; Magnetic Stripe; Barcode; RFID; OMR and OCR. 1. Introduction The computer vision and digital image processing are fast- growing fields that are essential in many aspects of other areas like multimedia, artificial intelligence, robotics and much more. More sophisticated imaging systems can handle inter plot the re- sults of image analysis and describe the various objects and their connections in the scene. Image analysis involves the study of segmentation, feature extraction, and classification techniques. The advent of technology has charted an amazing and noble growth curve for the past two centuries. This improvement is very clearly evident to computer engineers and researchers alike, in the way interactions between man and machine have grown. The advancement comes to a great distance from punch cards in the 1950s to the graphical interface in the past decade to human- based interactions in the present and the future. Throughout the last few years, it is comfortable in using the mouse and the key- board to assist as interfaces between us and the infinite essentiality, the computer. However, the ability to use human-based communi- cations to interact with a computer would make things easier me- chanically for the user, but would be difficult to succeed for the researchers. If continuous research in these areas makes ground- breaking achievements, the interactions with computers would increasingly seem like interactions between people. The natural and straightforward means of interactions between humans, speech, and handwriting come into focus as human-based interactions. Humans interact quite naturally with each other over writing and speech, but doing this with computers would make things exciting and easier to the user. Moreover, focusing on handwriting as a medium of communication with the computers, it can simply be stated that just like all good things it throws up its difficulties to be faced. Text recognition may offer the solution to complex problems and help ease the drudgery involved in managing complex image files. Conversion of scanned images into text document can enable ma- nipulation through word processing applications. Optical Charac- ter Recognition (OCR) has gained a momentum since the need for digitizing or converting scanned images of machine-printed or hand written text (numerals, letters, and symbols), into a format recognized by computers (such as ASCII). OCR has been widely used as the basic application of different learning methods in ma- chine learning literature [1]. 2. Automatic identification Various techniques using speech recognition, radio frequency, magnetic stripe, bar code, and Optical Mark Recognition (OMR), and OCR fulfill the automation needs in various applications [2]. 2.1. Magnetic stripe The magnetic stripe can store a large amount of data. Specially designed machine readers can access the information stored on the magnetic stripe. The information cannot read by humans. The storage has been done by changing the tiny iron-based magnetic particles [3]. The magnetic stripe may also be called as magstripe or swipe card which is shown in figure 1. They are commonly used in credit/debit cards, shopping reward points card, identity card, etc. They are used for electronic payment or controlling the access of business premises with the aid of Radio-Frequency Iden- tification (RFID) tag. Fig. 1: Magnetic Stripe Card.