Ariyaka: A PALI Alphabet Recognition Script

Gautam, Neha; Sharma, Rahul; Hazrati, Garima

doi:10.1109/cicn.2015.65

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…shows that the model is performing well in classifying unseen data and the is no overfitting fig [14]…”

Section: Since Validation Accuracy Is Greater Than Training Accuracy Itmentioning

confidence: 97%

Computational Approach for Character Identification and Retrieval of Grantha Inscription

Dhivya¹

2020

IJATCSE

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Computational Epigraphy plays a significant role to extract the information about the cultural heritage of various civilizations of the dynasty which includes rich political thoughts, literature, mythology, architecture, medicine, script evolution, etc. Traditionally the study of the epigraph is performed manually and hence the quantitative analysis of script is required. The proposed methodology recognizes the ancient Grantha script. Our approach, builds a K-Nearest Neighbor predictive model, replacing the data to function as a classification basis. Automatically the value of K is calculated as it varies for several data, and is optimized in terms of efficiency. The advancement in computer vision techniques has supported various types of real-time application tasks. One such task is the ability to recognize a character or normally referred to as OCR. There are also several types of algorithms that can be transposed into an OCR. This work aims at figuring out the process behind the OCR model by using K-Nearest Neighbor algorithm and a basic OCR system is developed to identify Grantha script for generating and comparing actual results. The initial step is to train the machine to recognize the ancient character by using the classification algorithm K-nearest neighbor with Laplacian of Gaussian (LoG) filter. This framework obtained the prediction rate of 91% with 800 samples per character for recognizing the ancient script

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“…shows that the model is performing well in classifying unseen data and the is no overfitting fig [14]…”

Section: Since Validation Accuracy Is Greater Than Training Accuracy Itmentioning

confidence: 97%

Computational Approach for Character Identification and Retrieval of Grantha Inscription

Dhivya¹

2020

IJATCSE

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“…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%

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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“…Various types of modern and ancient scripts have been recognized such as, English [5], Chinese [6], Devanagari [3], Bangla [7] etc. under modern script, and Brahmi [8,9], Ariyaka [10], Akharamuni [11] etc. under ancient category.…”

Section: Introductionmentioning

confidence: 99%

Zig-zag diagonal and ANN for English character recognition

Gautam¹

2019

IJATCSE

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Pattern recognition is the most popular are in the modern world. Significant progress has made in English character and digits recognition technology. However, the performance of the existing system of English character and digits recognition can be increased. This study is suggested a novel method, zig-zag diagonal feature extraction method along with ANN to increase the recognition rate of the English character and digit recognition system. The proposed method of this method is tested on an official dataset of English characters (EMNIST) and English digits (MNIST), achieving a 99.8% accuracy and 94% accuracy, respectively.

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Ariyaka: A PALI Alphabet Recognition Script

Cited by 3 publications

References 1 publication

Computational Approach for Character Identification and Retrieval of Grantha Inscription

Computational Approach for Character Identification and Retrieval of Grantha Inscription

Recognition of Brahmi Words by Using Deep Convolutional Neural Network

Zig-zag diagonal and ANN for English character recognition

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