Jyoti Mahajan and Rohini Mahajan/ Elixir Comp. Sci. & Engg. 69 (2014) 23716-23719 23716 Introduction A character can be written in a number of ways differing in shape and properties, such as Tilt, stroke, Cursively etc. Although there are different types of Fonts which have different italics and different in any commonly used Word Processing Application Software yet while perceiving any text written in a variety of ways, humans can easily recognize each character because the human perception extracts the features of the image of the character in retina that define a character’s shape in an overall fashion but modeling the human perception model in machines, this task becomes a hard problem. Optical Character Recognition, usually abbreviated to OCR, is the mechanical or electronic translation of images of handwritten, typewritten or printed text into machine-editable text. The images are usually captured by a scanner. However, throughout the text, we would be referring to printed text by OCR. Data Entry through OCR has faster speed, more accuracy, and generally more efficiency than keystroke data entry. Basically, there are three types of OCR. In Offline Handwritten text is produced by a person by writing with a pencil on a paper medium and then scanned into digital format using scanner. Online Handwritten Text is written directly on a digitizing tablet using stylus. The output is a sequence of x-y coordinates that express pen position as well as other information such as pressure (exerted by the writer) and speed of writing. Machine Printed Text can be found commonly in daily use produced by offset processes, such as laser, inkjet and many more. Optical Character Recognition is used to convert different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data. The OCR technology can also be used for Processing checks, Documenting library materials and Storing documents, searching text and extracting data from paper based documents Review Of Literature An Optical Character recognition system based on Artificial Neural Networks (ANNs) is trained using the Back Propagation algorithm where each typed English letter is represented by binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input, are fed to an ANN. After the Feed Forward Algorithm which gives workings of a neural network the Back Propagation Algorithm follows which compromises of Training, Calculating Error, and Modifying Weights. Artificial neural networks are commonly used to perform character recognition due to their high noise tolerance. The systems have the ability to yield excellent results. The feature extraction step of optical character recognition is the most important. A poorly chosen set of features will yield poor classification rates by any neural network. The most straight forward way of describing a character is by the actual raster image. Another approach is to extract certain features that still A Proposed method for designing an intelligent system for optical handwritten character recognition Jyoti Mahajan 1,* and Rohini Mahajan 2 1 Government College of Engineering & Technology, Jammu. 2 School of Engineering, Shri Mata Vaishno Devi University, Katra ABSTRACT 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. Other areas—including recognition of hand printing, cursive handwriting, and printed text in other scripts (especially those with a very large number of characters)--are still the subject of active research. 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 recognize all handwritten cursive script accurately (greater than 98%). It is necessary to understand that OCR technology is a basic technology also used in advanced scanning applications. Due to this, an advanced scanning solution can be unique and patented and not easily copied despite being based on this basic OCR technology. In this paper, an intelligent system for “OPTICAL CHARACTER RECOGINITION” using Artificial Neural Network based approach and a Feature Extraction algorithm before an ANN can be applied for classification of characters which promises to provide increased efficiency for the character recognition is proposed. © 2014 Elixir All rights reserved. Elixir Comp. Sci. & Engg. 69 (2014) 23716-23719 Computer Science and Engineering Available online at www.elixirpublishers.com (Elixir International Journal) ARTICLE INFO Article history: Received: 7 November 2013; Received in revised form: 20 April 2014; Accepted: 28 April 2014; Keywords Latin-script, Typewritten, Recognition. Tele: E-mail addresses: rohinimahajan11@yahoo.in © 2014 Elixir All rights reserved