Line Based Robust Script Identification for IndianLanguages

Kumar, B. Vijay; Bera, Aniket; Patnaik, Tushar

doi:10.7763/ijiee.2012.v2.79

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Script classification using these features is done in a two-layer feed-forward network. B.Kumar et al [17] have used topological, structural features with rule based classifier for line based multi-script identification. The use of Gabor features has not been discussed for Line-wise script identification to the best of our knowledge.…”

Section: Figure 1 Sample Of Punjabi Text Written In Gurmukhi Script Interpersed With English and Hindi Linesmentioning

confidence: 99%

Gabor Features Based Script Identification of Lines within a Bilingual/ Trilingual Document

Rani

Dhir

Lehal³

et al. 2014

IJAST

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The OCR technology for Indian documents is in emerging stage and most of these Indian OCR systems can read the documents written in only a single script. As many commercial and official documents of different states of India are tri-lingual in nature, therefore identification of script and/ or language is one of the elementary tasks for multi-script document recognition. A script recognizer simplifies the task of multi-lingual OCR by improving the accuracy and reducing the computational complexity. This script recognition may be at line, word or character level depending on interlacing of different scripts at different levels. This paper presents the effectiveness of Gabor Filter banks with kNN, SVM and PNN classifiers to identify the scripts at line level from such trilingual documents. The experiments show that Gabor features with SVM classifier achieve a recognition rate of 99.85% for trilingual documents.

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Section: Figure 1 Sample Of Punjabi Text Written In Gurmukhi Script Interpersed With English and Hindi Linesmentioning

confidence: 99%

Gabor Features Based Script Identification of Lines within a Bilingual/ Trilingual Document

Rani

Dhir

Lehal³

et al. 2014

IJAST

View full text Add to dashboard Cite

show abstract

“…In Ding et al, [20], a method that uses a combined analysis of several discriminating statistical features to classify Oriental and European scripts is presented. Tan et al, [21] has proposed a rotation invariant texture feature extraction method for automatic script and language identification from document images using multiple channel (Gabor) filters and Gray level co-occurrence matrices for seven languages: Chinese, English, Greek, Koreans, Malayalam, Persian and Russian. A Busch et al, [22] has presented the use of texture features (gray level cooccurrence matrix and Gabor energy features) for determining the script of a document image.…”

Section: Local and Global Approaches On Non-indian Scriptsmentioning

confidence: 99%