Handwritten Hindi character recognition using k-means clustering and SVM

Gaur, Akanksha; Yadav, Sunita

doi:10.1109/ettlis.2015.7048173

Cited by 43 publications

(14 citation statements)

References 19 publications

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“…The work has also compared the efficiency of using various features such as Marte-Bunke feature, PHOG, Gaber, G-PHOG and claims to get highest accuracy of 94.51% for GPHOG features. Another approach [19] of character recognition used k-mean clustering for features extraction and features are used on SVM classifier with linear kernel. The highest accuracy which was claimed with SVM classifier is 95.86% and that with Euclidean distance is 81.7%.…”

Section: Machine Learning Based Braille Transliteration Of Odia Langumentioning

confidence: 99%

Machine Learning Based Braille Transliteration of O dia Language

Jha*,

Parvathi

2020

IJITEE

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Braille transliteration of natural languages is required for providing a better opportunity of learning and creating opportunities of ceceity people. It allows a bigger diaspora of non-blind teachers to have written communication with blind people. The present paper proposes a method of Braille transliteration of Handwritten and printed Odia characters automatically into Braille. The current work proposes a method of Braille transliteration of Handwritten Odia text with industry applicable accuracy. The method first preprocesses the text and then segments it into characters and then uses an SVM classifier trained on HOG features of Odia handwritten characters to predict characters and maps the predicted printable character to its corresponding Braille with a very good accuracy. The method can further be used with text to speech engines to help the blind students use this technique with refreshable Braille having audio facility to listen the same.

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Section: Machine Learning Based Braille Transliteration Of Odia Langumentioning

confidence: 99%

Machine Learning Based Braille Transliteration of O dia Language

Jha*,

Parvathi

2020

IJITEE

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“…That is the reson for why algorithms applied in image recognition are effective to approach the character recognition systems. Correctly, many algorithms hidden Markov model [1], neural network [2], or support vector machine (SVM) [6] confirm the high performance; however, most researches have referred to SVM algorithm as the best method illustrated in [4][6] [8]. Normally, both training and recognition processes are implemented on high power PC to tackle the complicated training SVM algorithm and long converge time.…”

Section: Training and Recognitionmentioning

confidence: 99%

“…As the result, almost researches have utilized the standard resources such as USPS, or MNIST for handwritten numbers as illustration in [1] and [2]. Besides, some researches have approached Greek characters [3], India characters [4], or Thailand characters [5] following the authors' mother languages. That is root cause of hardly estimating or comparing the performance of character recognition systems.…”

Section: Introductionmentioning

confidence: 99%

Isolated vietnamese handwriting recognition embedded system applied combined feature extraction method

Van

Huu

Hoang

2015

2015 International Conference on Advanced Technologies for Communications (ATC)

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In this research, an embedded system approaching isolated Vietnamese handwriting upper case recognition is illustrated. Obtained results from experiments have experienced that proposed feature extraction method, a major step of complete character recognition scheme, combining contour profile and projection histogram techniques is superior to others. Furthermore, the full conceptual system performance is evaluated on the embedded system, namely BeagleBoard-xM board, to confirm that recognition probability is up to more 90% over 27000 characters over thirty different persons.

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“…Agglomerative Hierarchical Clustering is a bottom up approach where each observation starts in its own cluster, and pairs of clusters are merged as one moves up in the hierarchy [13].The result of the hierarchical methods is a dendrogram, representing the nested grouping of objects. There are different methods for agglomeration such as single, complete, average methods.…”

Section: Agglomerative Clusteringmentioning

confidence: 99%

Optimal Clustering Technique for Handwritten Nandinagari Character Recognition

Guruprasad¹,

Majumdar²

2017

IJCATR

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Abstract:In this paper, an optimal clustering technique for handwritten Nandinagari character recognition is proposed. We compare two different corner detector mechanisms and compare and contrast various clustering approaches for handwritten Nandinagari characters. In this model, the key interest points on the images which are invariant to Scale, rotation, translation, illumination and occlusion are identified by choosing robust Scale Invariant Feature Transform method(SIFT) and Speeded Up Robust Feature (SURF) transform techniques. We then generate a dissimilarity matrix, which is in turn fed as an input for a set of clustering techniques like K Means, PAM (Partition Around Medoids) and Hierarchical Agglomerative clustering. Various cluster validity measures are used to assess the quality of clustering techniques with an intent to find a technique suitable for these rare characters. On a varied data set of over 1040 Handwritten Nandinagari characters, a careful analysis indicate this combinatorial approach used in a collaborative manner will aid in achieving good recognition accuracy. We find that Hierarchical clustering technique is most suitable for SIFT and SURF features as compared to K Means and PAM techniques.

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Handwritten Hindi character recognition using k-means clustering and SVM

Cited by 43 publications

References 19 publications

Machine Learning Based Braille Transliteration of O dia Language

Machine Learning Based Braille Transliteration of O dia Language

Isolated vietnamese handwriting recognition embedded system applied combined feature extraction method

Optimal Clustering Technique for Handwritten Nandinagari Character Recognition

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