Recognition of Isolated Handwritten Kannada Numerals Based on Image Fusion Method

Rajput, G. G.; Hangarge, Mallikarjun

doi:10.1007/978-3-540-77046-6_19

Cited by 29 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combination of horizontal and vertical summation vector is used to form feature vector. Work is also carried out for Numeral recognition in many scripts like Bangla [8], Oriya [9] and Kannada [10]. The first most important issue or step in recognition process is the separation of character using segmentation.…”

Section: Related Workmentioning

confidence: 99%

Marathi Numeral Identification System in Devanagari Script Using 1D Discrete Cosine Transform

Vaidya¹,

Joshi²,

Bhalerao³

2017

IJIES

View full text Add to dashboard Cite

Recognition of handwritten Marathi character/digits is comparatively a tough task as compared to English. It has several types of applications including the postal code reading and sorting, banks check amount processing. In this paper a novel feature extraction and selection method is proposed for the recognition of isolated handwritten Marathi numbers which is based on one dimensional Discrete Cosine Transform (DCT) algorithm. The scanned document is pre-processed and segmented to create isolated numerals. Features for each numeral can be calculated after normalizing the numeral image to 32 × 32 size. Based on these reduced features, the numerals are classified into appropriate groups. Neural network is used for classification of numerals based on the extracted features. The results of proposed method are compared with the results obtained using Discrete Wavelet Transform and zonal discrete cosine transform (ZDCT). The proposed approach gives improved results as compared to zonal DCT and DWT method. Experimental results shows accuracy observed for the method is 90.30% with normalized numeral image of size 32 × 32.

show abstract

Section: Related Workmentioning

confidence: 99%

Marathi Numeral Identification System in Devanagari Script Using 1D Discrete Cosine Transform

Vaidya¹,

Joshi²,

Bhalerao³

2017

IJIES

View full text Add to dashboard Cite

show abstract

“…In [5], offline handwritten numeral recognition for six popular Indian scripts is reported. In [4,6,7], attempts have also been made to recognize offline handwritten Kannada numerals. However, to our knowledge, no attempt has been made to recognize online handwritten numerals of Indian scripts.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal LDA in PCA Transformed Subspace

Prasad

Sarojadevi²,

Ramakrishnan

2010

2010 12th International Conference on Frontiers in Handwriting Recognition

View full text Add to dashboard Cite

The paper addresses the effectiveness of orthogonal linear discriminant analysis (OLDA) in a principal component analysis (PCA) transformed subspace. The performance of the technique is studied for writer independent recognition of online handwritten Kannada numerals. Experiments show that the performance of LDA and OLDA are better in a PCA transformed subspace compared to that of the original feature space. In addition, the recognition accuracies of the system with OLDA are marginally better than that of LDA in both the original feature space and the PCA transformed subspace. An average recognition accuracy of 96.9% is achieved on a database collected from 69 writers. To our knowledge, this is the first ever reported work on recognition of online handwritten Kannada numerals.

show abstract

“…Dhandra [11,16] have proposed a method based on directional density feature which is thinning free, independent of size, and font styles of the English numeral and they also proposed Template method with various similarity /dissimilarity measures used for handwritten Kannada numeral recognition system. The recognition system of printed and handwritten for Kannada/Marathi numerals using Fourier descriptor with SVM classifier have been proposed by Rajput [14,19] The concept of Script independent handwritten numerals recognition system is suggested by Dhandra [12] using wavelet feature and structural feature. Ramtake [18] From the literature survey, it is evident that handwritten numerals recognition is still a fascinating area of research to design a robust and efficient Optical Character Recognition (OCR) system in general and mixed numeral recognition system in particular.…”

Section: Introductionmentioning

confidence: 99%

Printed and Handwritten Kannada Numerals Recognitionusing Directional Stroke and Directional Density With KNN

Bv¹,

RG²,

Hangarge³

2011

Int J Mach Intell

Self Cite

View full text Add to dashboard Cite

Abstract-In real life applications number of document contains printed as well as handwritten numerals in a single document. The process of recognition of such mixed numeral with respect to single OCR is complicated task. In this paper, we present a novel method for recognition of printed and handwritten Isolated Kannada numerals using single OCR system. We considered Directional Stroke and Directional density based feature for recognition system is proposed. The proposed system extracts Directional stroke on various angles and Directional density/profile on four side of the numeral image. Further, the stroke and density based extracted feature is feed for recognition systems. A Euclidian distance criteria and k-NN classifier is employed to classify the numeral class. A total 5000 numeral images, which includes 4000 for handwritten images and 1000 for printed images considered for experiments and overall accuracy found to 98.04%. The novelty of the proposed method is thinning free, and without size normalization.

show abstract

Recognition of Isolated Handwritten Kannada Numerals Based on Image Fusion Method

Cited by 29 publications

References 13 publications

Marathi Numeral Identification System in Devanagari Script Using 1D Discrete Cosine Transform

Marathi Numeral Identification System in Devanagari Script Using 1D Discrete Cosine Transform

Orthogonal LDA in PCA Transformed Subspace

Printed and Handwritten Kannada Numerals Recognitionusing Directional Stroke and Directional Density With KNN

Contact Info

Product

Resources

About