Developing a commercial grade Tamil OCR for recognizing font and size independent text

Liyanage, Chamila; Nadungodage, Thilini; Weerasinghe, Ruvan

doi:10.1109/icter.2015.7377678

Cited by 10 publications

(7 citation statements)

References 7 publications

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“…Deals with three feature extraction techniques in order to grasp features from various Tamil characters possessing variations in style and shape Tamil text recognition by using KNN classifier [9] Overall 91%…”

Section: Approachmentioning

confidence: 99%

“…The value for K in K-Means Clustering was taken as 25 since it was acceptable choice in terms of various factors. The overall recognition accuracy is 92.77% and 89.66% on the training set and test set respectively.Elakkiya et al[9] proposed an approach for Tamil text recognition by using KNN classifier. This involves a template creation stage where images of every letters are gathered and split into connected component images.…”

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confidence: 99%

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Effective Tamil Character Recognition Using Supervised Machine Learning Algorithms

Suriya

Nivetha

Pavithran

et al. 2023

EAI Endorsed Trans e-Learn

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Computational linguistics is the branch of linguistics in which the techniques of computer science are applied to the analysis and synthesis of language and speech. The main goals of computational linguistics include: Text-to- speech conversion, Speech-to-text conversion and Translating from one language to another. A part of Computational Linguistics is the Character recognition. Character recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. Character recognition methodology mainly focuses on recognizing the characters irrespective of the difficulties that arises due to the variations in writing style. The aim of this project is to perform character recognition for of one of the complex structures of south Indian language ‘Tamil’ using a supervised algorithm that increases the accuracy of recognition. The novelty of this system is that it recognizes the characters of the Predominant Tamil Language. The proposed approach is capable of recognizing text where the traditional character recognition systems fails, notably in the presence of blur, low contrast, low resolution, high image noise, and other distortions. This system uses Convolutional Neural Network Algorithm that are able to exact the local features more accurately as they restrict the receptive fields of the hidden layers to be local. Convolutional Neural Networks are a great kind of multi-layer neural networks that uses back-propagation algorithm. Convolutional Neural Networks are used to recognize visual patterns directly from pixel images with minimal preprocessing. This trained network is used for recognition and classification. The results show that the proposed system yields good recognition rates.

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Section: Approachmentioning

confidence: 99%

mentioning

confidence: 99%

Effective Tamil Character Recognition Using Supervised Machine Learning Algorithms

Suriya

Nivetha

Pavithran

et al. 2023

EAI Endorsed Trans e-Learn

View full text Add to dashboard Cite

show abstract

“…In such work, a comparative study among the different classifiers has been conducted by concluding that the Hoeffding tree was the most performing one by reaching 73% accuracy. Another approach to support character recognition was based on the use of the Tesseract OCR open source engine to train a Tamil OCR model [47]. In particular, a segmentation approach was adopted by using a box file system.…”

Section: Optical Character Recognitionmentioning

confidence: 99%

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

2021

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Internet-based communication systems have become an increasing tool for spreading misinformation and propaganda. Even though there exist mechanisms that are able to track unwarranted information and messages, users made up different ways to avoid their scrutiny and detection. An example is represented by the mixed-code language, that is text written in an unconventional form by combining different languages, symbols, scripts and shapes. It aims to make more difficult the detection of specific content, due to its custom and ever changing appearance, by using special characters to substitute for alphabet letters. Indeed, such substitute combinations of symbols, which tries to resemble the shape of the intended alphabet’s letter, makes it still intuitively readable to humans, however nonsensical to machines. In this context, the paper explores the possibility of identifying propaganda in such mixed-code texts over the Internet, centred on a machine learning based approach. In particular, an algorithm in combination with a deep learning models for character identification is proposed in order to detect and analyse whether an element contains propaganda related content. The overall approach is presented, the results gathered from its experimentation are discussed and the achieved performances are compared with the related works.

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“…It's well-known that Indic languages have many complexities and variations of characters which makes OCR systems hard to develop. But in the past few years, multiple studies have been conducted integrating Tesseract OCR engine for character recognition using different low resource languages such as Tamil [10], Hindi [11], Bengali [12] and Urdu [13].…”

Section: International Journal On Advances In Ict For Emerging Regionsmentioning

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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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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Developing a commercial grade Tamil OCR for recognizing font and size independent text

Cited by 10 publications

References 7 publications

Effective Tamil Character Recognition Using Supervised Machine Learning Algorithms

Effective Tamil Character Recognition Using Supervised Machine Learning Algorithms

An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet

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

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