Language Identification of Kannada, Hindi and English Text Words Through Visual Discriminating Features

Padma, M. C.; Vijaya, P. A.

doi:10.1080/18756891.2008.9727609

Cited by 20 publications

(17 citation statements)

References 6 publications

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“…If the document consists of text words in other than the anticipated languages, our previous algorithm fails to identify the type of the language by misclassifying the text words. Keeping the drawback of the previous method [15] in mind, we have proposed a system that would more accurately identify and separate different language portions of Kannada, Hindi and English documents and also to classify the portions of the document in other than these three languages into a fourth class category -OTHERS, as our intension is to identify only Kannada, Hindi and English. The system identifies the three languages in four stages: in the first stage Hindi is identified, in the second stage Kannada is identified, in the third stage English is identified and in the fourth and the last stage, languages other than Kannada, Hindi and English are grouped into fourth class category OTHERS without identifying the type of that language as our main aim is to focus only on Kannada, Hindi and English languages.…”

Section: Previous Workmentioning

confidence: 99%

Language Identification of Kannada, Hindi and English Text Words Through Visual Discriminating Features

Padma¹,

Vijaya²

2008

IJCIS

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Section: Previous Workmentioning

confidence: 99%

Language Identification of Kannada, Hindi and English Text Words Through Visual Discriminating Features

Padma¹,

Vijaya²

2008

IJCIS

Self Cite

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“…Padma et.al. [11,12] have proposed a method based on visual discriminating features to identify Kannada, Hindi and English text lines. Vipin Gupta et.al.…”

Section: Literary Surveymentioning

confidence: 99%

A Novel Way of Identifying Telugu, Tamil and English Scripts by Priority check using Discerning Features

Bodduluri¹,

Krishna²,

Babu³

et al. 2014

IOSRJECE

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“…In this context, researchers have made a number of attempts to discriminate the Han and Latin script (Spitz [6], Lu and Tan [7]) at the document level and exploited many Indian scripts at line level and word level (Pal and Chaudhury [8], Pal and Chaudhury [9], Padma and Nagabhushan [10], Dhandra et al [11]). However, all the techniques reported in the literature are script dependent.…”

Section: B Script Recognitionmentioning

confidence: 99%

Segmentation of Tri-Lingual Documents

Mahesha¹,

N²,

Nandini³

2017

IJARCSSE

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Abstract-Physical layout analysis intends to study the arrangement of layouts or locations of the regions present in a document image before understanding it. Before extracting the text or information from a document image, page segmentation (layout analysis) techniques need to be applied to identify the exact layout (area) where the text or image resides. In Page Segmentation, Top-down methods are simple and efficient but fail in non Manhattan layouts. In contrast, Bottom-up approaches adapt non Manhattan layouts easily than the top down approaches, but heavily depend on the threshold, parameters and extensive computations for layout identification. On the other hand, Hybrid methods (Bruel [31], Bruel [32]) suits well for layout identification by eliminating the dependency on threshold and parameters. But this analyzes the white background of the image with small white rectangles and merges them to locate the content blocks. Merging of small white rectangles makes the identification process tedious since large number of small white rectangles gets involved in the image. In addition, this approach heavily relies on heuristics for merging operations, which affects the segmentation rate considerably. In all the above reported methods (Bottom up and Hybrid approaches), connected component analysis (requires more number of pixel visits) is required to identify black and white components from the image. Therefore, the above shortcomings motivated this research towards designing a White Space analysis technique which eliminates the usage of the connected component analysis (to identify white spaces), heuristics, threshold and prior knowledge. As a result, in this thesis, Rectangular White Space Analysis (RWSA) technique has been proposed to grab all the white spaces over the image in a single scan over the image with minimum pixel visits, and the white spaces are merged together without the assumptions of heuristics and threshold to segment the layouts. Moreover, two statistical properties have also been proposed in this thesis, to separate the text blocks and images from the identified layouts and this hybrid approach has been explained in the subsequent section.

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Language Identification of Kannada, Hindi and English Text Words Through Visual Discriminating Features

Cited by 20 publications

References 6 publications

Language Identification of Kannada, Hindi and English Text Words Through Visual Discriminating Features

Language Identification of Kannada, Hindi and English Text Words Through Visual Discriminating Features

A Novel Way of Identifying Telugu, Tamil and English Scripts by Priority check using Discerning Features

Segmentation of Tri-Lingual Documents

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