Converting printed Sinhala documents to formatted editable text

Ajward, Shahina; Jayasundara, N. C. Y.; Madushika, Sarasi; Ragel, Roshan

doi:10.1109/iciafs.2010.5715649

Cited by 11 publications

(5 citation statements)

References 2 publications

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“…Considering literature, Neural Network based Sinhala OCR systems have been developed in recent years [4], [5], [6]. In 2013, the Sri Lanka Institute of Information Technology conducted a research based on applying neural networks for Sinhala optical character recognition [4].…”

Section: Related Workmentioning

confidence: 99%

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Estimating the Effects of Text Genre, Image Resolution and Algorithmic Complexity needed for Sinhala Optical Character Recognition

Anuradha

Liyanage

Weerasinghe

2021

Int J on Adv. in ICT for Emerging Countries

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While optical character recognition for Latin based scripts have seen near human quality performance, the accuracy for the rounded scripts of South Asia still lags behind. Work on Sinhala OCR has mainly reported on performance on constrained classes of font faces and so been inconclusive. This paper provides a comprehensive series of experiments using conventional machine learning as well as deep learning on texts and font faces of diverse types and in diverse resolutions, in order to present a realistic estimation of the complexity of recognizing the rounded script of Sinhala. While texts of both old and contemporary books can be recognized with over 87% accuracy, those in old newspapers are much harder to recognize owing to poor print quality and resolution.

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Section: Related Workmentioning

confidence: 99%

“…In this study they have only focused on 36 characters in the alphabet. Another Sinhala OCR application integrating neural networks was developed by a local research group [5]. These studies mainly focused on the character level accuracies and not on word accuracies.…”

Section: Related Workmentioning

confidence: 99%

Estimating the Effects of Text Genre, Image Resolution and Algorithmic Complexity needed for Sinhala Optical Character Recognition

Anuradha

Liyanage

Weerasinghe

2021

Int J on Adv. in ICT for Emerging Countries

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“…Similarly, Hewavitharana et al [184] used hidden Markov models for off-line Sinhala character recognition. Statistical approaches with histogram projections for Sinhala character recognition is proposed by Hewavitharana and Kodikara [185], by Ajward et al [186], and by Madushanka et al [187]. Karunanayaka et al [188] also did off-line Sinhala character recognition with a use case for postal city name recognition.…”

Section: Optical Character Recognition Applicationsmentioning

confidence: 99%

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

Silva¹

2019

Preprint

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Sinhala is the native language of the Sinhalese people who make up the largest ethnic group of Sri Lanka. The language belongs to the globe-spanning language tree, Indo-European. However, due to poverty in both linguistic and economic capital, Sinhala, in the perspective of Natural Language Processing tools and research, remains a resource-poor language which has neither the economic drive its cousin English has nor the sheer push of the law of numbers a language such as Chinese has. A number of research groups from Sri Lanka have noticed this dearth and the resultant dire need for proper tools and research for Sinhala natural language processing. However, due to various reasons, these attempts seem to lack coordination and awareness of each other. The objective of this paper is to fill that gap of a comprehensive literature survey of the publicly available Sinhala natural language tools and research so that the researchers working in this field can better utilize contributions of their peers. As such, we shall be uploading this paper to arXiv and perpetually update it periodically to reflect the advances made in the field.

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“…In the OCR approach for Sinhala character recognition which has been proposed by Ajward et al [1] preprocessing stages follows a neural network classifier. Extracted features such as centroid, bounding box are used to crop each individual image rather than feeding the characters directly to the classifier.…”

Section: Fig 1 a Similarity Between Sinhala And Tamil Charactersmentioning

confidence: 99%

A fuzzy based model to identify printed Sinhala characters

Gunarathna

Chamikara

Ragel

2014

7th International Conference on Information and Automation for Sustainability

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Character recognition techniques for printed documents are widely used for English language. However, the systems that are implemented to recognize Asian languages struggle to increase the accuracy of recognition. Among other Asian languages (such as Arabic, Tamil, Chinese), Sinhala characters are unique, mainly because they are round in shape. This unique feature makes it a challenge to extend the prevailing techniques to improve recognition of Sinhala characters. Therefore, a little attention has been given to improve the accuracy of Sinhala character recognition. A novel method, which makes use of this unique feature, could be advantageous over other methods. This paper describes the use of a fuzzy inference system to recognize Sinhala characters. Feature extraction is mainly focused on distance and intersection measurements in different directions from the center of the letter making use of the round shape of characters. The results showed an overall accuracy of 90.7% for 140 instances of letters tested, much better than similar systems.

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Converting printed Sinhala documents to formatted editable text

Cited by 11 publications

References 2 publications

Estimating the Effects of Text Genre, Image Resolution and Algorithmic Complexity needed for Sinhala Optical Character Recognition

Estimating the Effects of Text Genre, Image Resolution and Algorithmic Complexity needed for Sinhala Optical Character Recognition

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

A fuzzy based model to identify printed Sinhala characters

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