Why neural network is used in character recognition

Optical character recognition, usually abbreviated to OCR, is the mechanical or electronic translation of images of handwritten, typewritten or printed text usually captured by a scanner into machine-editable text. OCR is a field of research in pattern recognition, artificial intelligence and machine vision. Though academic research in the field continues, the focus on OCR has shifted to implementation of proven techniques.

Optical character recognition using optical techniques such as mirrors and lenses and digital character recognition using scanners and computer algorithms were originally considered separate fields. Because very few applications survive that use true optical techniques, the OCR term has now been broadened to include digital image processing as well.

This system is the base for many different types of applications in various fields, many of which we use in our daily lives. Cost effective and less time consuming, businesses, post offices, banks, security systems, and even the field of robotics employ this system as the base of their operations. Images of. Forms can be scanned through an imaging scanner, faxed, or computer generated to produce the bitmap. OCR is less accurate than optical mark recognition but more accurate than intelligent character recognition.

Images of hand-printed characters are extracted from a bitmap of the scanned image. ICR recognition of numeric characters is much more accurate than the recognition of letters. However, proven form design methods outlined later in this paper can minimize ICR errors. OMR technology detects the existence of a mark, not its shape. OMR forms usually contain small ovals, referred to as bubbles, or check boxes that the respondent fills in.

OMR cannot recognize alphabetic or numeric characters. OMR is the fastest and most accurate of the data collection technologies. It is also relatively user-friendly. The accuracy of OMR is a result of precise measurement of the darkness of a mark, and the sophisticated mark discrimination algorithms for determining whether what is detected is an erasure or a mark. Almost all U. Many modern recognition engines can recognize EB fonts that are not printed with magnetic ink.

However, since background designs can interfere with optical recognition, the banking industry uses magnetic ink on checks to ensure accuracy.

A barcode is a machine-readable representation of information. Barcodes can be read by optical scanners called barcode readers or scanned from an image using software.

A 2D barcode is similar to a linear, one- dimensional barcode, but has more data representation capability. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase.

In more practical terms neural networks are non-linear statistical data modeling tools. They can be used to model coplex relationships between inputs and outputs or to find patterns in data. Neural networks have seen an explosion of interest over the last few years, and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology and physics.

Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced. This sweeping success can be attributed to a few key factors:. Power: Neural networks are very sophisticated modeling techniques capable of modeling extremely complex functions.

In particular, neural networks are nonlinear. For many years linear modeling has been the commonly used technique in most modeling domains since linear models have well-known optimization strategies. Where the linear approximation was not valid which was frequently the case the models suffered accordingly.

Neural networks also keep in check the curse of dimensionality problem that bedevils attempts to model nonlinear functions with large numbers of variables. Ease of use: Neural networks learn by example. The neural network user gathers representative data, and then invokes training algorithms to automatically learn the structure of the data. Although the user does need to have some heuristic knowledge of how to select and prepare data, how to select an appropriate neural network, and how to interpret the results, the level of user knowledge needed to successfully apply neural networks is much lower than would be the case using for example some more traditional nonlinear statistical methods.

One of the most typical problems to which a neural network is applied is that of optical character recognition. Recognizing characters is a problem that at first seems extremely simple- but it's extremely difficult in practice to program a computer to do it. And yet, automated character recognition is of vital importance in many industries such as banking and shipping.

The U. We may have used scanning software that can take an image of a printed page and generate an ASCII document from it. Ting T. Wood C. This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in to check access. For example, see G. Martin, M. Rashid, D. Chapman, J. Hanson, J.

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