A modified Thinning Algorithm for Handwritten Tamil Characters

2013 Fifth International Conference on Advanced Computing (ICoAC), 2013

In machine character recognition the traditional clustering method Kmeans have been applied to provide K-clusters which recognize the character into the corresponding cluster. In this paper, we propose a weighted K-Means clustering technique for Character Recognition. And the superiority of this proposed clustering technique over the commonly used K-means clustering technique is extensively demonstrated with six Tamil character data set.

Indian International Conference on Artificial Intelligence, 2009

Segmentation is division of something into smaller parts and one of the Component of character recognition system. Separation of characters, words and lines are done in Segmentation from text documents. character recognition is a process which allows computers to recognize written or printed characters such as numbers or letters and to change them into a form that the computer can use. the accuracy of OCR system is done by taking the output of an OCR run for an image and comparing it to the original version of the same text. The main aim of this paper is to find out the various text line segmentations are Projection profiles, Weighted Bucket Method. Proposed method is horizontal projection profile and connected component method on Handwritten Kannada language. These methods are used for experimentation and finally comparing their accuracy and results.

2013

Handwritten recognition is an area of research where many researchers have presented their work and is still an area under research to achieve higher accuracy. In past collecting, storing and transmitting information in form of handwritten script was the most convenient way and is still prevailing as a convenient medium in the era of digital technology. As technology has advanced tablet and many similar devices allows humans to input data in form of handwriting. Use of paper to write handwritten text, converting to an image using scanner, identifying handwritten characters from the image is known as off-line handwritten text recognition is a challenging area due to the fact that different people will have different style of writing and all scripts have their own character set and complexities to write text. Many researchers have presented their work and many algorithms are proposed to recognize handwritten and printed characters. One can trace extensive work for off-line handwritten recognition for English and Arabic script. This paper presents review of work to recognize off-line handwritten text for various Indian language scripts. Paper reviews methodologies with respect to the phases of character recognition.

ELEKTRIKA- Journal of Electrical Engineering

The thinning algorithm is one of the approaches of identifying each character printed on the car plate. Malaysian car plate characters appear in different character sizes, styles, customized printed characters etc. These variations contribute to difficulty in thinning successfully segmented and extracted license plate characters for recognition. To address these problems, an improved thinning operation for Malaysian car plate character recognition is proposed. In this algorithm, samples from segmented and extracted license plates are used for a thinning operation which is passed to Zhang-Suen thinning algorithm that could not guarantee one pixel thick and then to single pixelate algorithm that provides one pixel width of character for recognition. From the simulation, the result obtained has clearly proven to be the best for character recognition systems with least number of white pixels (777 pixels) and 0.26% redundant pixel left in the medial curve.

2021

The process of recognizing the script written by different people with different handwriting in different languages with the help of machines is called Off-line handwritten character recognition (OHCR). It is difficult for machines to recognize the script in same language written in different localities and circumstances. It is an important factor that how far machine can achieve the accuracy in recognizing these handwritten records. In this article, it is examined the off-line handwritten records in Malayalam language. Malayalam is the Dravidian language which is used in the Kerala state of India, Lakshadweep and Mahi. This paper presents a comprehensive survey of various techniques with its accuracy rate on Malayalam off-line handwriting recognition for the past few years. Different feature extraction and classification techniques used in Malayalam off-line handwriting recognition are pointed out. Then databases chosen for these purposes are also discussed.

International Journal of Image, Graphics and Signal Processing, 2013

In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets.

2015

Handwritten character recognition is always a frontier area of research in the field of pattern recognition. There is a large demand for OCR on hand written documents in Image processing. Even though, sufficient studies have performed in foreign scripts like Arabic, Chinese and Japanese, only a very few work can be traced for handwritten character recognition mainly for the south Indian scripts. OCR system development for Indian script has many application areas like preserving manuscripts and ancient literatures written in different Indian scripts and making digital libraries for the documents. Feature extraction and classification are essential steps of character recognition process affecting the overall accuracy of the recognition system. This paper presents a brief overview of digital image processing techniques such as Feature Extraction, Image Restoration and Image Enhancement. A brief history of OCR and various approaches to character recognition is also discussed in this paper.

This paper describes a system for recognizing offline handwritten Tamil characters using support vector machine (SVM). Data samples are collected from different writers on A4 sized documents. They are scanned using a flat bed scanner at a resolution of 300 dpi and stored as gray-scale images. Various preprocessing operations are performed on the digitized image to enhance the quality of the image. Pixel densities are calculated for 64 different zones of the image and these values are used as the features of a character. These features are used to train the SVM. The SVM is tested for the first time to recognize handwritten Tamil characters. The system has achieved a very good recognition accuracy of 82.04% on the handwritten Tamil character database.

In the field of pattern recognition, HCR is one of the most intricate and tricky area. Plenty of works were proposed for foreign languages but a few works exists for south Indian languages due to the complex shape and varying writing styles of individuals. This paper introduces a review of offline and online recognition of different natural languages. HCR is an optical character recognition, which convert the textual document in to machine readable format. To attain 99.9 % accuracy in the field of HCR is very difficult. The efficiency of HCR depend the features extracted and the classifier used.

Thinning of character images is a big challenge. Removal of strokes or deformities in thinning is a difficult problem. In this paper, we have proposed a medial axis based thinning strategy used for performing skeletonization of printed and handwritten character images. In this method, we have used shape characteristics of text to get skeleton of nearly same as the true character shape. This approach helps to preserve the local features and true shape of the character images. The proposed algorithm produces one pixel width thin skeleton. As a by-product of our thinning approach, the skeleton also gets segmented into strokes in vector form. Hence further stroke segmentation is not required. Experiment is done on printed English and Bengali characters and we obtain less spurious branches comparing with other thinning methods without any post processing.