A Comprehensive Survey on OCR Techniques for Kannada Script (original) (raw)
Related papers
Lipi Gnani - A Versatile OCR for Documents in any Language Printed in Kannada Script
ArXiv.org, 2019
A Kannada OCR, named Lipi Gnani, has been designed and developed from scratch, with the motivation of it being able to convert printed text or poetry in Kannada script, without any restriction on vocabulary. The training and test sets have been collected from over 35 books published between the period 1970 to 2002, and this includes books written in Halegannada and pages containing Sanskrit slokas written in Kannada script. The coverage of the OCR is nearly complete in the sense that it recognizes all the punctuation marks, special symbols, Indo-Arabic and Kannada numerals and also the interspersed English words. Several minor and major original contributions have been done in developing this OCR at the different processing stages such as binarization, line and character segmentation, recognition and Unicode mapping. This has created a Kannada OCR that performs as good as, and in some cases, better than the Google's Tesseract OCR, as shown by the results. To the knowledge of the authors, this is the maiden report of a complete Kannada OCR, handling all the issues involved. Currently, there is no dictionary based postprocessing, and the obtained results are due solely to the recognition process. Four benchmark test databases containing scanned pages from books in Kannada, Sanskrit, Konkani and Tulu languages, but all of them printed in Kannada script, have been created. The word level recognition accuracy of Lipi Gnani is 4% higher on the Kannada dataset than that of Google's Tesseract OCR, 8% higher on the datasets of Tulu and Sanskrit, and 25% higher on the Konkani dataset.
OCR for printed Kannada text to machine editable format using database approach
WSEAS Transactions on Computers archive, 2008
This paper describes an Optical Character Recognition (OCR) system for printed text documents in Kannada, a South Indian language. The proposed OCR system for the recognition of printed Kannada text, which can handle all types of Kannada characters. The system first extracts image of Kannada scripts, then from the image to line segmentation then segments the words into sub-character level pieces. For character recognition we have used database approach. The level of accuracy reached to 100%.
Kannada Character Recognition System: A Review
Intensive research has been done on optical character recognition ocr and a large number of articles have been published on this topic during the last few decades. Many commercial OCR systems are now available in the market, but most of these systems work for Roman, Chinese, Japanese and Arabic characters. There are no sufficient number of works on Indian language character recognition especially Kannada script among 12 major scripts in India. This paper presents a review of existing work on printed Kannada script and their results. The characteristics of Kannada script and Kannada Character Recognition System kcr are discussed in detail. Finally fusion at the classifier level is proposed to increase the recognition accuracy.
Sadhana-academy Proceedings in Engineering Sciences, 2007
Optical Character Recognition (OCR) systems have been effectively developed for the recognition of printed characters of non-Indian languages. Efforts are on the way for the development of efficient OCR systems for Indian languages, especially for Kannada, a popular South Indian language. We present in this paper an OCR system developed for the recognition of basic characters (vowels and consonants) in printed Kannada text, which can handle different font sizes and font types. Hu’s invariant moments and Zernike moments that have been progressively used in pattern recognition are used in our system to extract the features of printed Kannada characters. Neural classifiers have been effectively used for the classification of characters based on moment features. An encouraging recognition rate of 96.8% has been obtained. The system methodology can be extended for the recognition of other south Indian languages, especially for Telugu.
An Overview of OCR Research in Indian Scripts
This paper gives an overview of the ongoing research in optical character recognition (OCR) systems for Indian language scripts. This survey paper has been felt necessary when the work on developing OCRs for Indian scripts is very promising, and is still in emerging status. The aim of this paper is to provide a starting point for the researchers entering into this field. Peculiarities in Indian scripts, present status of the OCRs for Indian scripts, techniques used in them, recognition accuracies, and the resources available, are discussed in detail. Examples given in this paper are based on authors' work on developing a character recognition system for Telugu, a south Indian language.
Machine Recognition of Printed Kannada Text
5th International Workshop, DAS 2002 , Proceedings, 2002
This paper presents the design of a full fledged OCR system for printed Kannada text. The machine recognition of Kannada characters is dificult due to similarity in the shapes of different characters, script complexity and non-uniqueness in the representation of diacritics. The document image is subject to line segmentation, word segmentation and zone detection. From the zonal information, base characters, vowel modifiers and consonant conjuncts are separated. Knowledge based approach is employed for recognizing the base characters. Various features are employed for recognising the characters. These include the coefficients of the Discrete Cosine Transform, Discrete Wavelet Transform and Karhunen-Louve Transform. These features are fed to different classifiers. Structural features are used in the subsequent levels to discriminate confused characters. Use of structural features, increases recognition rate from 93% to 98%. Apart from the classical pattern classification technique of nearest neighbour, Artificial Neural Network (ANN) based classifiers like Back Propagation and Radial Basis Function (RBF) Networks have also been studied. The ANN classifiers are trained in supervised mode using the transform features. Highest recognition rate of 99% is obtained with RBF using second level approximation coefficients of Haar wavelets as the features on presegmented base characters.
OCR in Indian scripts: A survey
2005
India is a multilingual country. A significantly large number of scripts are used to represent these languages. A desire of vision researchers is to develop an integrated optical character recognition (OCR) system, which will be able to process all such scripts. Such a development, if objectified, will not only enable faster flow of information across the country, but also have a profound effect on its scientific and economical development. Courageous endeavours have been successfully made towards the development of systems capable of recognizing machine-printed or handwritten characters and/or numerals. However, most Indian scripts do not have an integrated OCR system. Further, the development of a unified system, which is capable of processing all Indian scripts is still a dream. This article presents a survey of the current literature on the development of OCR's in Indian scripts. Reviewing the basis of and the motivation towards the development of OCR system, the article analyzes the various methodologies employed in general purpose pattern recognition systems. A critical analysis of the work towards OCR systems in Indian languages, with pointers towards possible future work, is also presented.
Review on OCR for Handwritten Indian Scripts Character Recognition
Natural language processing and pattern recognition have been successfully applied to Optical Character Recognition (OCR). Character recognition is an important area in pattern recognition. Character recognition can be printed or handwritten. Handwritten character recognition can be offline or online. Many researchers have been done work on handwritten character recognition from the last few years. As compared to non-Indian scripts, the research on OCR of handwritten Indian scripts has not achieved that perfection. There are large numbers of systems available for handwritten character recognition for non-Indian scripts. But there is no complete OCR system is available for recognition of handwritten text in any Indian script, in general. Few attempts have been carried out on the recognition of Devanagari, Bangla, Tamil, Oriya and Gurmukhi handwritten scripts. In this paper, we presented a survey on OCR of these most popular Indian scripts.
Optical Character Recognition or OCR is the electronic translation of handwritten, typewritten or printed text into machine translated images. Optical Character Recognition (OCR) is a very important task in Pattern Recognition. Foreign languages, especially English character recognition has been extensively studied by many researches but due to complication of Indian Languages like Hindi ,Punjabi ,teulgu ,malyalam etc. the research work is very limited and constrained. This paper presents the research work related to all Indian languages, various approaches to character recognition along with some applications of character recognition is also discussed in this paper. The aim of this paper is to provide an overview of the research going on in Indian script OCR systems. This survey paper has been felt necessary when the research on OCRs for Indian scripts is still a challenging task. Hence, a brief introduction to the general OCR and typical steps in the development of an OCR are give...