Paper Info
Title
Bag-Of-Visual Words For Word-Wise Video Script Identification: A Study
Abstract
Use of multiple scripts for information communication through various media is quite common in a multilingual country. Optical character recognition of such document images or videos assists in indexing them for effective information retrieval. Hence, script identification from multi-lingual documents/images is a necessary step for selecting the appropriate OCR, due the absence of a single OCR system capable of handling multiple scripts. Script identification from printed as well as handwritten documents is a well-researched area, but script identification from video frames has not been explored much. Low resolution, blur, noisy background, to mention a few are the major bottle necks when processing video frames, and makes script identification from video images a challenging task. This paper examines the potential of Bag-of-Visual Words based techniques for word-wise script identification from video frames. Two different approaches namely, Bag-Of-Features (BoF) and Spatial Pyramid Matching (SPM), using patch based SIFT descriptors were considered for the current study. SVM Classifier was used for analysing the three popular south Indian scripts, namely Tamil, Telugu and Kannada in combination with English and Hindi. A comparative study of Bag-of-Visual words with traditional script identification techniques involving gradient based features (e. g. HoG) and texture based features (e. g. LBP) is presented. Experimental results shows that patch-based features along with SPM outperformed the traditional techniques and promising accuracies were achieved on 2534 words from the five scripts. The study reveals that patch-based feature can be used for scripts identification in-order to overcome the inherent problems with video frames.
Year
DOI
Venue
2015
10.1109/IJCNN.2015.7280631
2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
Field
DocType
ISSN
Scale-invariant feature transform,Bag-of-words model in computer vision,Pattern recognition,Computer science,Optical character recognition,Search engine indexing,Feature extraction,Speech recognition,Vector quantization,Artificial intelligence,Telugu,Scripting language
Conference
2161-4393
Citations 
PageRank 
References 
3
0.36
2
Authors
5
Name
Order
Citations
PageRank
Nabin Sharma113211.55
Ranju Mandal2314.28
Rabi Sharma331.03
Umapada Pal41477139.32
Michael Blumenstein534827.40