Handwritten Kannada Vowel Character Recognition Using Crack Codes and Fourier Descriptors

Rajput, G. G.; Horakeri, Rajeswari

doi:10.1007/978-3-642-25725-4_15

Cited by 10 publications

(7 citation statements)

References 16 publications

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“…A character recognition accuracy of 56% was achieved using a Multi-layer feedforward neural network with one hidden layer. In Rajput et al, [2] binary images of numerals are created by scanning and a size of 40 x 40pixel image is created after normalization. The line between the object pixels and the background (the crack) is computed and these are termed as Crack Codes.…”

Section: -Literature Surveymentioning

confidence: 99%

An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier

Ramesh¹,

B²,

Bhat³

et al. 2021

JIST

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Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision. The event of the handwritten digit is generous. With a wide opportunity, the issue of handwritten digit recognition by using computer vision and machine learning techniques has been a well-considered upon field. The field has gone through an exceptional turn of events, since the development of machine learning techniques. Utilizing the strategy for Support Vector Machine (SVM) and Principal Component Analysis (PCA), a robust and swift method to solve the problem of handwritten digit recognition, for the Kannada language is introduced. In this work, the Kannada-MNIST dataset is used for digit recognition to evaluate the performance of SVM and PCA. Efforts were made previously to recognize handwritten digits of different languages with this approach. However, due to the lack of a standard MNIST dataset for Kannada numerals, Kannada Handwritten digit recognition was left behind. With the introduction of the MNIST dataset for Kannada digits, we budge towards solving the problem statement and show how applying PCA for dimensionality reduction before using the SVM classifier increases the accuracy on the RBF kernel. 60,000 images are used for training and 10,000 images for testing the model and an accuracy of 99.02% on validation data and 95.44% on test data is achieved. Performance measures like Precision, Recall, and F1-score have been evaluated on the method used.

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Section: -Literature Surveymentioning

confidence: 99%

An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier

Ramesh¹,

B²,

Bhat³

et al. 2021

JIST

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“…In3 3systems of3 vcomputer 3vision, so3 3many approaches are3 3there to3 3find the3 3number of contours3 3of a3 3digit 3imagea. Some3 3of the3 3approaches are3 3Freeman chain3 3code approach [22], two-dimensional3 coding system3 [23], polygonal3 3coding, and3 3the connected3 component labeling3 3algorithm3 [24] and3 etc. From3 3them, the3 3connected component3 3labeling algorithm3 3is most3 popular.…”

Section: Fig 3 Sample Scanned Document Of Digit Imagesmentioning

confidence: 99%

Efficient Handwritten Digit Classification using User-defined Classification Algorithm

Reddy¹,

Babu²

2018

International Journal on Advanced Science, Engineering and Information Technology

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In automatic numeral digit recognition system, feature selection is most important factor for achieving high recognition performance. To achieve this, the present paper3 proposed3 system3 for isolated handwritten3 3numeral recognition3 3using number3 of contours3, skeleton features, Number of watersheds, and ratio between the numbers of foreground3 3pixels in3 3upper half3 3part and3 3lower half-part3 3of the numerical digit image. Based on these features the present paper designed user-defined classification algorithm for handwritten digit recognition. To find the effectiveness of the proposed features, these features are given as an input for standard classification algorithms like k-nearest neighbor classifier, Support Vector Machines and other classification algorithms and evaluate the results. The experimental result proves that the proposed features are well suited for handwritten digit recognition for both user and standard classification algorithms. The3 3novelty of3 3the proposed3 3method is3 3size 3invariant.

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“…Also, in [7], a technique utilizes a number of statistical methods to perform machine print recognition. In addition, several approaches that are based on Neural Networks and Support Vector Machine (SVM) have been investigated for recognition of on-line and off-line handwritten Arabic and Hindi numerals [8][9][10][11][12][13][14][15]. Likewise, Hidden Markov Models have also been adopted for recognition of off-line handwritten numerals [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Off-Line Arabic (Indian) Numbers Recognition Using Expert System

Malallah¹,

Saeed²,

Aziz³

et al. 2016

ijacsa

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Abstract-This paper proposes an effective approach to automatic recognition of printed Arabic numerals which are extracted from digital images. First, the input image is normalized and pre-processed to an acceptable form. From the preprocessed image, components of the words are segmented into individual objects representing different numbers. Second, the numerical recognition is performed using an expert system based on a set of if-else rules, where each set of rules represents the categorization of each number. Finally, rigorous experiments are carried out on 226 random Arabic numerals selected from 40 images of Iraqi car plate numbers. The proposed method attained an accuracy of 97%.

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Handwritten Kannada Vowel Character Recognition Using Crack Codes and Fourier Descriptors

Cited by 10 publications

References 16 publications

An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier

An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier

Efficient Handwritten Digit Classification using User-defined Classification Algorithm

Off-Line Arabic (Indian) Numbers Recognition Using Expert System

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