4. Electronics - IJECEIERD - OCR - Patange V

International Journal of Electronics, Communication & Instrumentation Engineering Research and Development (IJECIERD) ISSN 2249-684X Vol.2, Issue 3 Sep 2012 38-44 © TJPRC Pvt. Ltd.,

O.C.R. – A METHOD OF COMPUTES & WORKS ON THE VISION OF SCANNED IMAGE 1 1

PATANGE V.V. & 2DESHMUKH B.T.

P.G. student & Lecturer, Electronics & Telecommunication Engineering, S.T.B. College of Engineering, Tuljapur. Maharashtra, India 2

Professor, Electrical Engineering Department, J.N.E.C. Aurangabad, Maharashtra, India

ABSTRACT A simple optical character recognition system involves four step procedures of pre-processing, feature extraction, classification and post-processing. In pre-processing stage, binarization, boundary detection and segmentation of image is performed. Thinning and resizing of the segmented image is done for template formation. Image templates acts as feature vectors which are stored as reference database. In classification stage, the test features of vector are compared with all the reference feature vectors and feature vector with maximum match is declared as the best matched template. In post-processing stage, the character associated with the best match template is put in suitable word processor file.

KEYWORDS: OCR, Image Processing, Image, Scanning.

INTRODUCTION Optical character recognition is one of the most successful applications in automatic pattern recognition. Optical character recognition has been very active field for research and development. Since mid 1950’s techniques developed were able to recognize high quality printed text documents or neatly written hand written text. The current research in optical character recognition is addressing documents that are not well handled by the available systems. Also efforts are being made to achieve lower substitution error rates and rejection rates even on good quality machine printed text.

Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance. Our interest in character recognition is to recognize machine printed digits or characters. From available feature extraction methods reported in literature, a newcomer to the field is faced by the following question. Which feature extraction method is best for the given application? This question leads to characterize the given feature extraction methods, so that the most promising method will be sorted out.

One could argue that there is only a limited number of independent numbers of features that can be extracted from a character image, so that which set of features is used is not so important. However, the extracted features must be invariant to the expected distortions and variations that the characters must

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O.C.R. – A Method of Computes & Works on the Vision of Scanned Image

have in a specific application. Also the phenomenon called course of dimensionality cautions us that with limited training set, the number of features must be kept reasonably small if a statistically classifier is to be used.

Feature extraction is an important step in achieving good performance of optical character recognition system. However, the steps in the systems must be optimized for obtaining the best possible performance and these steps are not independent. The choice of feature extraction method limits the nature and output of the preprocessing step. Some feature extraction methods work on gray level subimages of single characters, while others work on solid four or eight connected symbols segmented from the binary raster image, thinned image, thinned symbols, skeletons or symbols contours.

The type of extracted features must match the requirements of the chosen classifier. Graph descriptions or grammar based descriptions of the characters are well suited for structural or syntactic classifiers. Discrete features that may assume only, say, two or three distinct values are ideal for decision trees. Real valued vectors are for statistical classifiers. However, multiple classifiers may be used, either as a multistage classification scheme or parallel classifiers, where a combination results is used to decide the final classification. In that case, features of more than one type or format may be extracted from the input characters.

Implementation of all the feature extraction methods is an enormous task. In addition, the performance also depends on the type of classifier used. Different features may need different classifiers.

In brief, the criteria for selection of feature extraction from patterns are: 1.

Number of features must be limited.

2.

Features must be invariant to the expected distortion & variations in images.

3.

The type of extracted features must match the requirement of chosen images.

4.

The extracted features from binary image are sufficient for better classification.

An optical character system typically consists of the following processing steps: 1.

Grey level scanning at an appropriate resolution, typically 300-1000 dots per inch.

2.

Pre-processing a.

Binarization (two level threshold), using a global or locally adaptive method.

b.

Segmentation to isolate individual characters representation.

3.

Feature extraction

4.

Recognition using on or more classifiers.

5.

Contextual verification or post-processing.

Patange V.V. & Deshmukh B.T.

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PROBLEM DEFINITION Most government agencies and companies have kept proof data and documentations which are passed certain period of time and exchanged electronic forms by the regulation of an office management.

The method that saving relevant documents by scanning or entering manually on computer was used for document digitalizing. This dissertation work deal with the study and implementation of various processing stages of optical printed character recognition using template matching.

Character recognition is important in pattern recognition and digital image processing. This is implemented to recognize the characters from documents. The features can be extracted from image by performing mathematical or algorithmic process on pictorial data. In this work, binarization, noise elimination and segmentation methods are implemented in the pre-processing stage. In the text processing step, two feature extraction methods have been implemented. Zoning and distance measurement techniques are implemented using minimum distance techniques classifiers and using other statistical classifiers.

TECHNOLOGY & METHODOLOGY Methods of Character Recognition: There are two methods of character recognition.

1.

On-line character recognition

2.

Off-line character recognition

On-Line Character Recognition Method It deals with characters drawn usually on a graphics tablet using stylus and recognized immediately by the computer. It’s a bidirectional programming and a continuous linkage (On-line) between the input device (graphical tablet) and the computer. However, the character recognition algorithms of the online character recognition systems are simpler than the common offline methods.

Since, not only the schematic information is provided, but also the sequence, pressure information that makes the detection of each drawn stroke an easy task.

OFF-LINE CHARACTER RECOGNITION METHOD The off-line character method does not use a stylus and graphics tablet. Instead, character images written on a paper are captured via a scanner or a camera & stored in digitized form (binary or gray scale). Timing and dynamic information is highly degraded in a static specimen. This leads to a high degree of complexity in an off-line character recognition process. The off-line character recognition method can be implemented by using one of the following techniques: a)

Image processing technique.

b) Pattern Recognition technique.

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O.C.R. – A Method of Computes & Works on the Vision of Scanned Image

These techniques can be used to solve the problem encountered in pre-processing, Feature extraction, and specimen comparison and performance evaluation.

IMAGE ACQUISITION IN OFFLINE METHOD Image acquisition is a method of scanning a document so as to represent as a gray scale array.

This process contains 4 basic building blocks: 1. Detector 2. Light source 3. Scan lens 4. Document transport To capture an image, the object is placed in the scanning field. A light source floods the object with illumination and the forms an image on the detector. The detector consists of array elements each of which converts incident light into a charge or analog signals. The analog signal is then converted into digital image. Scanning is formed by the detector and the motion of the text objects with respect to detector. Normally detectors are photodiodes or charge coupled devices.

Feature Extraction Methods in Character Recognition The methodologies applied in character recognition are not different to that applied in any general image pattern recognition problem, with respect to image analysis and pattern recognition.

However, because of the particularity of the character recognition problem, the methodologies can be summarized as follows: a) Point by Point Global Comparison: This process involves a point by point comparison of all pixels that are stored with the pixels of the image. b) Global Transformation: This process involves moment calculations, Fourier and Rotation according to the principal axis of inertia. c) Extraction of local proprieties: This extraction can be achieved by various methods, e.g. by means of fixed masks of variable positions.

It is always prerequisites to thin the character and then apply the above methods of extraction. d) Structural Methods: Such methods include decomposition of the character into its constituent elements, the topological descriptions and reduction of character into graph.

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Patange V.V. & Deshmukh B.T.

CHARACTER RECOGNITION INTRODUCTION In the previous section, we have passed through a general introduction of character recognition, its various types, image acquisition and feature extraction. In this section, we will see how a character recognizer really works. What are the various steps to be followed to recognize the characters from a scanned image of a text document.

Processed Image

Image (Pixels)

Preprocessing

Classificatio n

Feature Selectio n

Feature Extractio n

Postprocessing

Recognized Character

Fig. 1 Overview of an optical character recognition system In general, character recognition schemes perform the same basic sequence of operations consisting of pre-processing, feature extraction, classification and post-processing as shown in figure above. Pre-processing includes all the steps that are necessary to bring the input data into a form acceptable to the upcoming portions of the sections. It includes binarization, boundary detection, segmentation, thinning and image resizing. In binarization, the color image is converted into gray scale image. Boundary of character image can be produced by emphasizing regions containing abrupt darklight transitions and de-emphasizing regions containing approximately homogeneous intensity.

Advantages: In off-line character recognition, a character image or document can be copied optically. There is no need of using a special writing to collect information, to avoid serious constraints on its application.

Off-line characters typically exhibit a much less cost ratio than that of on-line.

RESULTS As per the simulation, these are the results for the Optical Character. In the fig.2, the left hand side shows the scanned image from a scanner, which we take as an input. The right hand side of screenshot shows the converted picture which is editable.

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O.C.R. – A Method of Computes & Works on the Vision of Scanned Image

Fig. 2 Scanned Image & Converted Image And fig. 3, is one of the basic application of the system through which we can change the font, color and style etc.

Fig. 3 Result Font Toolbar

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CONCLUSIONS This system will be useful for the industries, institutes, banking sector where the old documents are of great importance. Optical character recognition system has been implemented with template matching and distance measurement method. In order to recognize the character efficiently, which is in mass document, the optical character recognition system will be very useful.

REFERENCES 1.

www.araa.asn.au/acra/acra2004/papers/cole.pdf

2.

Evaluation of OCR Algorithms for Images with Different Spatial Resolutions and Noises by Qing Chen

3.

http://en.wikipedia.org/wiki/Image_scanner

4.

http://www.geekpoint.net/threads/characteristics-present-in-visual-basic.5583/

5.

http://lavape.sourceforge.net/doc/html/AdvantVB.htm