Handwriting Recognition of Brahmi Script (an Artefact): Base of PALI Language

Gautam, Neha; Sharma, Rahul; Hazrati, Garima

doi:10.1007/978-3-319-30927-9_51

Cited by 6 publications

(8 citation statements)

References 9 publications

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“…Three types of samples: vowels, consonants, and consonantal vowels (compound characters), were used to measure the performance of the system, achieving accuracies of 93.45%, 93.45%, and 90.24%, respectively. Apart from Brahmi word recognition, in 2016, Gautam, et al [4] focused on Brahmi character recognition. The zone method was used to extract the features of the Brahmi characters, and the template matching method (coefficient correlation) was used for the classification of all extracted features, achieving an accuracy of 88.83%.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Automatic word recognition has become a research area of pattern recognition for many years because of its various applications and needs [2,3]. However, very few works have completed on the South and Central Asian scripts [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…In the work of Brahmi scripts recognition, the suitable features of the Brahmi scripts are first extracted and next, using the suitable classifier, the extracted features will be classified. For instance, geometric features [9] of the Brahmi scripts had been used as the suitable features for Brahmi scripts recognition, zoning method [4,11] was applied to obtain the features of the Brahmi scripts and template matching [4] and Artificial Neural Network (ANN) [11] were used as classifier to classify the extracted features.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Brahmi Words by Using Deep Convolutional Neural Network

Gautam¹,

Chai²,

Jose³

2020

Preprint

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Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the identification of characters, compound characters, and word. This study proposes the use of the deep convolutional neural network with dropout to recognize the Brahmi words. This study also proposed a DCNN for Brahmi word recognition and a series of experiments are performed on standard Brahmi dataset. The practical operation of this method was systematically tested on accessible Brahmi image database, achieving 92.47% recognition rate by CNN with dropout respectively which is among the best while comparing with the ones reported in the literature for the same task.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recognition of Brahmi Words by Using Deep Convolutional Neural Network

Gautam¹,

Chai²,

Jose³

2020

Preprint

Self Cite

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Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the identification of characters, compound characters, and word. This study proposes the use of the deep convolutional neural network with dropout to recognize the Brahmi words. This study also proposed a DCNN for Brahmi word recognition and a series of experiments are performed on standard Brahmi dataset. The practical operation of this method was systematically tested on accessible Brahmi image database, achieving 92.47% recognition rate by CNN with dropout respectively which is among the best while comparing with the ones reported in the literature for the same task.

show abstract

“…Three types of samples: vowels, consonants, and consonantal vowels (compound characters), were used to measure the performance of the system, achieving accuracies of 93.45%, 93.45%, and 90.24%, respectively. Apart from Brahmi word recognition, in 2016, Gautam, et al [5] focused on Brahmi character recognition. The zone method was used to extract the features of the Brahmi characters, and the template matching method (coefficient correlation) was used for the classification of all extracted features, achieving an accuracy of 88.83%.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the work of Brahmi scripts recognition, the suitable features of the Brahmi scripts were extracted and after that, the extracted features were classified by using the suitable classifier. For instance, geometric features [4], zoning method [5,6] was applied to obtain the features of the Brahmi scripts and extracted features were classified by some set of rules [4], template matching [5] and Artificial Neural Network (ANN) [6]. Some works have been found out to recognize the Brahmi script, but the performance of the existing system can be increased because exist works did not focus on finding out the best feature extraction techniques and classifier for achieving the highest accuracy.…”

Section: Introductionmentioning

confidence: 99%

Brahmi Word Recognition by Supervised Techniques

Gautam¹,

Chai²,

Afrin³

et al. 2020

Preprint

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Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words. Maximum accuracy suggested by system is 95.17% which is better than previously suggested studies.

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