Characterization of ancient document images composed by Arabic and Latin scripts

Zaghden, Nizar; Mullot, Rémy; Alimi, Adel M.

doi:10.1109/innovations.2011.5893801

Cited by 5 publications

(4 citation statements)

References 8 publications

(20 reference statements)

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“…Zaghden et al . [41] proposed a method to characterise Arabic and Latin ancient document images. The fractal dimension method is used to discriminate between ancient Arabic and Latin scripts.…”

Section: Related Workmentioning

confidence: 99%

Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

Ammar

Bouwmans

Zaghden

et al. 2020

IET Image Processing

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Section: Related Workmentioning

confidence: 99%

Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

Ammar

Bouwmans

Zaghden

et al. 2020

IET Image Processing

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“…A system for the discrimination between ancient document collections of Arabic and Latin scripts [4], provides 95.87 % recognition rate. The advantage of this method is that it can be easily implemented for the recognition of other ancient document collections and it can have better identification rates by providing relative features of each document base.…”

Section: Related Workmentioning

confidence: 99%

“…The mathematical background of feature extraction is highlighted in Sect. 4. Experimental results and performance analysis is covered in Sects.…”

Section: Introductionmentioning

confidence: 99%

Fourier Features for the Recognition of Ancient Kannada Text

Soumya

Kumar

2015

Advances in Intelligent Systems and Computing

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Optical Character Recognition (OCR) System for ancient epigraphs helps in understanding the past glory. The system designed here, takes a scanned image of Kannada epigraph as its input, which is preprocessed and segmented to obtain noise-free characters. Fourier features are extracted for the characters and used as the feature vectors for classification. The SVM, ANN, k-NN, Naive Bayes (NB) classifiers are trained with different instances of ancient Kannada characters of Ashoka and Hoysala period. Finally, OCR system is tested on epigraphical characters of 250 from Ashoka and 200 from Hoysala period. The prediction analysis of SVM, ANN, k-NN and NB classifiers is made using performance metrics such as Accuracy, Precision, Recall, and Specificity.

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“…Another simple classifier is a K-nearest neighbor classifier with a Euclidean distance measure between input images [26]. The algorithm caches all of the training samples, and predicts the response for a new sample by analyzing a certain number ( K ) of the nearest neighbors of the sample (using voting, calculating weighted sum etc.)…”

Section: K-nearest Neighbors (K-nn)mentioning

confidence: 99%

Performance Analysis of Random Forests with SVM and KNN in Classification of Ancient Kannada Scripts

Soumya¹,

Kumar²

2014

IJCT

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Ancient inscriptions which reveal the details of yester years are difficult to interpret by modern readers and efforts are being made in automating such tasks of deciphering historical records. The Kannada script which is used to write in Kannada language has gradually evolved from the ancient script known as Brahmi. Kannada script has traveled a long way from the earlier Brahmi model and has undergone a number of changes during the regimes of Ashoka, Shatavahana, Kadamba, Ganga, Rashtrakuta, Chalukya, Hoysala , Vijayanagara and Wodeyar dynasties. In this paper we discuss on Classification of ancient Kannada Scripts during three different periods Ashoka, Kadamba and Satavahana. A reconstructed grayscale ancient Kannada epigraph image is input, which is binarized using Otsu's method. Normalized Central and Zernike Moment features are extracted for classification. The RF Classifier designed is tested on handwritten base characters belonging to Ashoka, Satavahana and Kadamba dynasties. For each dynasty, 105 handwritten samples with 35 base characters are considered. The classification rates for the training and testing base characters from Satavahana period, for varying number of trees and thresholds of RF are determined. Finally a Comparative analysis of the Classification rates is made for the designed RF with SVM and k-NN classifiers, for the ancient Kannada base characters from 3 different eras Ashoka, Kadamba and Satavahana period.

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Characterization of ancient document images composed by Arabic and Latin scripts

Cited by 5 publications

References 8 publications

Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

Fourier Features for the Recognition of Ancient Kannada Text

Performance Analysis of Random Forests with SVM and KNN in Classification of Ancient Kannada Scripts

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