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International Journal of Advancements in Technology

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A Survey On Handwritten and Printed Kannada Numeral Recognition Technique

Vishweshwarayya C Hallur

Department of Computer Applications, Visvesvaraya Technological University, Angadi Institute of Technology & Management, Belgaum, Karnataka 590008, India. Email: [email protected]

Avinash A Malawade

Department of Computer Applications, Visvesvaraya Technological University, Angadi Institute of Technology & Management, Belgaum, Karnataka 590008, India. Email: [email protected]

Seema G Itagi

Department of Computer Applications, Visvesvaraya Technological University, Angadi Institute of Technology & Management, Belgaum, Karnataka 590008, India. Email: [email protected]

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Keywords

OCR, pre-processing, image extraction and classification.

Introduction

For the development of a high performance OCR algorithm has become essential. OCR research work has been undertaken by several researchers which aim at developing a high performance OCR algorithm.

The purpose behind an OCR is to identify and analyze a document image by dividing the page into line elements, further sub-dividing into words, and then into characters. These characters are compared with image patterns to predict the probable characters. Recognition of characters can be done either from printed documents or from hand written documents.

In particular, Kannada hand written OCR is more complicated than other related work. This is because Kannada numerals have more angles (curves). Challenges that researchers face during recognition process are due to the curve in the numerals and number of strokes and holes, sliding numerals, different writing styles so on.

The steps involved in character recognition comprise pre-processing, segmentation feature extraction and classification. There are three types of features, namely statistical, structural and hybrid can be analyzed there.

Researchers have come up with many approaches for the character recognition, however, many of them have surveyed in the paper. Apart from that, challenges and issues still prevailing in this even for future research has also been surveyed in these papers.

This paper is organized in the following manner; Preprocessing techniques are surveyed in section 2. Section 3 illustrates the various segmentation techniques available. In section 4 Feature extraction methods are explained. Various classification approaches are available explained in section 5. In section 6 the scope of our research in this area and conclusion.

Heading and Footnotes

There are so many numbers of tasks to be completed before performing character recognition. A hand written document must be scanned and converted into a suitable format for processing. Pre-processing consisting of a few types of sub processes to clean the document image and make it appropriate to carry the recognition process accurately. The sub process which gets involved in pre-processing are illustrated bellow:

i. Binarization
ii. Noise reduction
iii. Normalization
iv. Skew correcting, thinning and slant removal

Binarization

Binarizing in a method of transforming a gray scale image into a black and white image through thresholding [1][2]. Another approach, Otsu’s method may be used to perform histogram based thresholding [3][4] to get binarized image automatically. Most researchers use thresholding concepts to extract the fore ground image from back ground image [5][6][7]. Threshold value is fixed in this method by taking any value between two foreground gray code images. Histogram based thresholding approach can also be used to identify the local gray value contrast of image. This will help to extract text information from low quality documents.

Noise removal

Digital images are having tendency to many types of noises. Noise in a document image is due to poorly photocopied pages. Median filtering [8], Wiener Filtering method [9] and morphological operations can be performed to remove noise[10]. Intensity of the character images are replaced using Median filters [11]. Whereas images are smoothened using Gaussian filters [12].

Normalization

Normalization is the process of converting random sized images into a standard size. The Bicubic interpolation [3], linear sized normalization [4] and Java Image Class [13] normalization techniques could be used for standard sized images. The Roi-Extraction [14] method is used to get the single structural element from the image. In many works, input images are normalized to a size 40 * 40 after finding the bounding box of each hand written image for razing processing.

Skew Correction, Thinning and Slant Removal

Thinning is a pre-processor which results in single pixel width image to recognize the hand written character easily. It is applied repeatedly leaving only pixel wide linear representations of the image character. Cumulative Scalar product (CSP)[8] of windows text lock with Gabor filters has been used for thinning purpose. Morphology based thinning algorithm [15] and other thinning algorithms [1][16][17] has also been used for better symbol representation and to thin the character images. Skeletonization is the process of shedding off a pattern to as many pixels as possible without affecting the general shape of the pattern. Skew is inevitably introduced into the incoming document image during document scanning. Fourier spectrum [18], normalization [19] techniques are used for correction of the slant, angle stroke, width and vertical scaling.

Segmentation

Segmentation process is used to split the document images into lines, words and characters. Segmentation of the handwritten document is more complex than type written documents. Histogram profiles and connected components analysis [14][20] are used for line segmentation. In this segmentation process, paragraph space has been checked for identifying paragraphs. Image’s histogram is used to detect the horizontal line’s width [21]. Special space detection technique has been used for word segmentation. Histogram method is used to detect the both width of the words [15] and also to convert the image to glyph [6].

The vertical histogram profile methods [3] [1] is used to find spacing within the lines to identify the word boundary. Region probe algorithm [16] is used to get individual characters from the image. Modified cross counting techniques [12], histogram profile [14] and connected component analysis are also found in the segmentation problem.

Feature Extraction

There are three classes of feature extraction namely statistical features, Structural features and the hybrid features. Quantitative measurements are used in stasticale feature technique, where as structural techniques use qualitative measurements for feature extraction. In hybrid approach, these two techniques are combined and used for recognition.

Structural Technique

Scale invariant feature Transformation (SIFT)[14] is used to transfer the character image into a set of local features. 128 dimensions of SIFE features re identified from the character image. An image is converted to two tone image, and then converted into frame. The frame point obtained from the frame will process the vectors. The normalized feature vector (NFV) obtains the prototype from vector.

Statistical Technique

In the zone based method, pixel destiny is calculated for each zone. Then the pixel destiny is used for representing the features. The height and width of character pixels are counted using the encoding Binary variation approach. The process halts, when the top level of row and width is reached. A feature is extracted from it and a binary flag is set as per the approach.

All images are scaled to the same height and width using bilinear interpolation technique [20]. Sobel edge detection algorithm [20] is used to correct the unwanted portions. Octal graphs [22] are used to derive structural features like end point, holes, length, shape and curvature of individual stroke.

Boundaries of the images are traced using “eight-neighbor” adjustment method. The approach scans until it finds the boundary of image. Then, the Fourier description [11] is used to find the co-efficient and obtain the total number of boundaries. This number of invariant descriptors is given as important to a natural network for future classification.

Hybrid Technique

Hough transform [23] is used to detect the horizontal and vertical lines. They have been analyzing branch and position using another algorithm. Bilinear interpolation [24] is used to extract the features such as slant and strip.

Classification

The extracted features are given as the input to the classification process. There are some approaches are used to classify the character features in the existing systems such as K-nearest Neighbor approach, fuzzy system, neural network and so on. For all these approaches, a bag of key points extracted from the feature extraction approaches are used for classification.

Table

Conclusion

Maximum research work exists in the survey for Kannada Handwritten numeral recognition. However, there is no standard solution to identify all kannada numerals with reasonable accuracy. Different approaches has been used in each phase of recognition process, where as each approach provides solution only for few numeral sets. Challenges still prevails in the recognition of normal as abnormal writing, slanting numerals, similar shaped numerals, curves and so on during recognition process.

The following key challenges can be further explored in my future research work.

• Curves in the Kannada numeral

• Significant variation in writing styles.

• Difficulties faced in viewing angles, shadows and unique fonts.

Acknowledgements

Authors are grateful to Dr.R.S.Hegadi, Solapur University, Solapur for his valuable guidance and also grateful to Dr. Nagaraja, Director of Angadi Institute of technology and Management, Belgaum for his constant support for researchers.

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