Daily used At present, the system has an overall accuracy of 96.09%. and background. For a new text line, necessary features are extracted from the top and bottom profiles and the feature values obtained are compared with the stored knowledge base. implements image capturing techniques, optical character in different development stages, viewpoints, angles, and Multiple channel (Gabor) filters and grey level co-occurrence matrices are used in independent experiments in order to extract texture features. We have proposed to learn identifying the language of the text by thoroughly understanding the nature of top and bottom profiles of the printed text lines in these three languages.Experimentation conducted involved 800 text lines for learning and 600 text lines for testing. All features are extracted globally from a given text block which does not require any complex and reliable seg- mentation of the document image into lines and characters. Raspberry Pi. The objective of this paper is to propose visual clues based The k-nearest neighbor classifier is used to classify the test sample. mechanisms. The system shows a good performance for single font scripts printed on clear documents, In a multi-lingual country like India, a document may contain more than one script forms. The distinct characteristic features of Kannada, Hindi and English scripts are thoroughly studied from the nature of the top and bottom profiles. Next feature based on water reservoir principle, contour tracing, profile etc. For the Optical Character Recognition of such a document page it is necessary to identify different scripts from the document. sentences were selected and captured in different development stages, viewpoints, angles, This scheme employs hierarchical clas- sification which uses features consistent with human perception. documents and converts images as OCR text using tesseract and then translates the text by The feature extraction is achieved by finding the behavior of the characteristics of the top and bottom profiles of individual text lines. It is necessary to identify different script regions of the document in order to feed the document to the OCRs of individual language. tree classifier is used for simple character recognition. people have barriers in languages. Here, at first, the document is segmented into lines and then the lines are segmented into possible words. translated. The proposed method is trained to learn thoroughly the distinct features of each script. In the process of The proposed approach is based on the horizontal and vertical projection profile for the discrimination of the three scripts. single Bangla (Bengali) font. The framework and the portable hardware system developed takes images of printed A document page may contain two or more different scripts. The technique is tested on 100 handwritten document pages containing both Devnagari and Roman script words and 99.54% of words are identified with their true class. The results are very encouraging and prove the efficacy of the proposed model. Text line segmentation is a challenging task in Optical Character Recognition, due to writing style of writers and touching characters or Matra between lines. In this research work, this problem of recognizing the language of the. At the next stage, a sub-classification is performed based on script-specific features. In this work, an improved method is proposed for the recognition of such characters (especially Kannada characters), which can have spread in vertical and horizontal directions. In this paper, we present a rule-based language identifier tool for two closely related Indo-Aryan languages: Hindi and Magahi. India is having more than 22 official language, every script has its own characteristics and features based on their unique feature we can distinguish one language script with another. OCR to get quality output and improve the accuracy rate, introduced several pre-processing part because people have barriers in languages. The translation is an essential part because All figure content in this area was uploaded by Padma M C, Language Identification of Kannada, Hindi and English Text Words Through Visual Discriminating Features.pdf, All content in this area was uploaded by Padma M C on Jan 21, 2016, Language Identification of Kannada, Hindi and English Text Words Through Visual Discriminating Fe, International Journal of Computational Intelligence Systems, Vol.1, No. There is a significant lack of computational resources in this language where one can find only a Magahi POS tagger, Magahi monolingual corpus, and Magahi Morph Analyser available (Kumar et al., 2011;Kumar et al., 2012;and Kumar et al., 2016). by the non-computerized system. This rectangle can be interpreted as a two-dimensional, 3×3 structure of nine parts which we define as bricks. International Journal of Computational Intelligence Systems, Optical Character Translation Using Spectacles (OCTS), OPTICAL CHARACTER TRANSLATION USING SPECTACLES (OCTS), Automatic Language Identification System for Hindi and Magahi, English Transliteration of Kannada Words with Anusvara and Visarga, Transliteration of text input from Kannada to Braille and vice versa, Script Identification of Central Asia Based on Fused Texture Features, Multiwavelet and connected pixel based feature for handwritten Marathi characters, Segmentation of Merged Lines and Script Identification in Handwritten Bilingual Documents, A review on multilingual document analysis in Indian context, Identification of Devnagari and Roman Scripts from Multi-script Handwritten Documents, Script Identification from Indian Documents. In this paper, an intelligent feature based technique is reported, which automatically identifies the scripts of handwritten words from a document page, written in Devnagari script mixed with Roman script. In this paper an automatic scheme is presented to identify text lines of different Indian scripts from a document. like thinning and skeletonization is not necessary in our scheme and Also, some character occurrence statistics have been This causes practical difficulty in digitizing such a document, because the language type of the text should be pre-determined, before feeding it into a suitable Optical Character Recognition (OCR) system. The headline is common in Bangla and Devnagari but absent in Roman. The performance has turned out to be 98.5%. Several language identification tools have been developed in Indian languages such as (a) In 2008, OCRbased Language Identification tool was developed by Padma and Vijaya which gave 99% accuracy, In India, a document may contain text lines in more than one language forms. The method shows robustness with respect to noise, the presence of foreign characters or numerals, and can be applied to very small amounts of text, In this paper a complete OCR system is described for documents of In a multi-lingual country like India, a document page may contain more than one script form. For optical character recognition (OCR) of such a document page, it is necessary to separate the scripts before feeding them to their individual OCR systems. In a multi-lingual multi-script country like India, a single text line of a document page may contain words of two or more scripts. text by using Google Translator API.
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