Review Paper on Various Methodology of Text Extraction from Image
DOI:
https://doi.org/10.53555/nncse.v2i3.501Keywords:
Feature Extraction, HMM, MDF, Neural Networks, OCR, Recognition, Segmentation, SVMAbstract
This review presents the various text extraction techniques and also compares the research results of various researchers in the domain of text extraction. A generic character recognition system has different stages like noise removal, skew detection and correction, segmentation, feature extraction and character recognition. Input is digitized image containing any text, which is preprocessed to segment it into normalized individual word and letters. The OCR, Neural Network, SVM are the various methods for text extraction. Text extraction helps to preserve history by making information efficiently searchable, easily manageable without the need for human labor.
References
Pratibha A. Desai, Sumangala N Bhavikatti, Rajashekar Patil, "Neural Networks Based Offline Handwritten Character Recognition System", SARC-IRAJ InternationalConference,16thjune2013.
Aparna.A , Prof.I.Muthumani,” Optical Character Recognition for Handwritten Cursive English characters”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5, 2014.
Muhammad Naeem Ayyaz, Imran Javed and Waqar Mahmood 1 2: Handwritten Character Recognition Using Multiclass SVM Classification with Hybrid Feature Extraction, Pak. J. Engg. & Appl. Sci. Vol. 10, Jan., 2012 (p. 57-67).
Rohini B. Kharate *1, Dr.S.M.Jagade 2, Sushilkumar N. Holambe, “A Brief Review and Survey of Segmentation for Character Recognition”, International Journal of Engineering Sciences, 2(1) January 2013.
Shilpy Bansal, Mamta Garg, Munish Kumar, “A Technique for Offline Handwritten Character Recognition”, IJCAT International Journal of Computing and Technology, Volume 1, Issue 2, March 2014.
F. Camastra and A. Vinciarelli, “Combining Neural Gas and Learning Vector Quantization for Cursive Character Recognition”, Neurocomputing, vol. 51, 2003, pp. 147-159.
M. Blumenstein and X. Y. Liu, B. Verma, “A Modified Direction Feature for Cursive Character Recognition “.
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