Feature selection for an automated ancient Tamil script classification system using machine learning techniques

Suganya, T.; Murugavalli, S.

doi:10.1109/icammaet.2017.8186731

Cited by 12 publications

(4 citation statements)

References 10 publications

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“…This body of work has focused on optical character recognition and visual analysis [31][32][33][34] , writer identification [35][36][37] and text analysis [38][39][40][41][42][43][44] , stylometrics 45 and document dating 46 . It is only very recently that scholarship has begun to use deep learning and neural networks for optical character recognition [47][48][49][50][51][52][53][54][55] , text analysis 56 , machine translation of ancient texts [57][58][59] , authorship attribution 60,61 and deciphering ancient languages 62,63 , and been applied to study the form and style of epigraphic monuments 64 .…”

Section: Previous Workmentioning

confidence: 99%

Restoring and attributing ancient texts using deep neural networks

Assael

Sommerschield

Shillingford

et al. 2022

Nature

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Ancient history relies on disciplines such as epigraphy—the study of inscribed texts known as inscriptions—for evidence of the thought, language, society and history of past civilizations1. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. Here we present Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian’s workflow. The architecture of Ithaca focuses on collaboration, decision support and interpretability. While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool. Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history. This research shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history.

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Section: Previous Workmentioning

confidence: 99%

Restoring and attributing ancient texts using deep neural networks

Assael

Sommerschield

Shillingford

et al. 2022

Nature

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“…Finally, several works have used machine learning to study ancient inscriptions, focusing on assistive tools (Roued-Cunliffe, 2010), optical character recognition and visual analysis (Terras and Robertson, 2006;Garz et al, 2014;Soumya and Kumar, 2014;Shaus, 2017;Hussien et al, 2015;Amato et al, 2016;Can et al, 2016;Suganya and Murugavalli, 2017;Palaniappan and Adhikari, 2017;Avadesh and Goyal, 2018), writer identifica-tion Panagopoulos et al, 2009;Faigenbaum-Golovin et al, 2016), text analysis (Rao et al, 2009bLee and Haug, 2010;Vatri and McGillivray, 2018), and machine translation (Pagé-Perron et al, 2017;Luo et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Restoring ancient text using deep learning: a case study on Greek epigraphy

Sommerschield¹,

Prag²

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Ancient History relies on disciplines such as Epigraphy, the study of ancient inscribed texts, for evidence of the recorded past. However, these texts, "inscriptions", are often damaged over the centuries, and illegible parts of the text must be restored by specialists, known as epigraphists. This work presents PYTHIA, the first ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks. Its architecture is carefully designed to handle longterm context information, and deal efficiently with missing or corrupted character and word representations. To train it, we wrote a nontrivial pipeline to convert PHI, the largest digital corpus of ancient Greek inscriptions, to machine actionable text, which we call PHI-ML. On PHI-ML, PYTHIA's predictions achieve a 30.1% character error rate, compared to the 57.3% of human epigraphists. Moreover, in 73.5% of cases the ground-truth sequence was among the Top-20 hypotheses of PYTHIA, which effectively demonstrates the impact of this assistive method on the field of digital epigraphy, and sets the state-of-the-art in ancient text restoration.

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“…The method is finally tested on standard benchmark Caltech‐256 database, which results in satisfactory performance for image classification problem. Suganya and Murugavalli () have developed a system that used firefly algorithm in performing supervised FS on features based on shape and Hough transform. A classification accuracy of 91.77% is attained for handwritten Tamil character recognition.…”

Section: Related Workmentioning

confidence: 99%

A clustering‐based feature selection framework for handwritten Indic script classification

et al. 2019

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In India, which has numerous officially recognized scripts, there is a primary need for categorizing the documents on the basis of the scripts used therein. Identification of script used in a document is essential for its effective handling both manually and digitally. Identification of script in a document image is an important research problem in the pattern recognition field, which, at times, suffers from the issue of growing dimensionality of the feature vector and requires an efficient feature selection technique. Keeping this fact in mind, in this paper, we propose a clustering‐based filter feature selection framework in order to extract an optimal and effective feature subset from the original feature vector. The present feature selection methodology is evaluated on a script classification problem involving handwritten documents in 12 major Indic scripts. Experiments are done at word‐level, text‐line‐level, and block‐level. Experiments demonstrate that a reasonable increment in classification accuracy has been realized using comparatively lesser number of features. The proposed framework for feature selection is computationally inexpensive and can be applied to other pattern recognition problems as well.

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Feature selection for an automated ancient Tamil script classification system using machine learning techniques

Cited by 12 publications

References 10 publications

Restoring and attributing ancient texts using deep neural networks

Restoring and attributing ancient texts using deep neural networks

Restoring ancient text using deep learning: a case study on Greek epigraphy

A clustering‐based feature selection framework for handwritten Indic script classification

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