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Analysis of Text Identification Techniques Using Scene Text and Optical Character Recognition

Analysis of Text Identification Techniques Using Scene Text and Optical Character Recognition

Monica Gupta, Alka Choudhary, Jyotsna Parmar
Copyright: © 2021 |Volume: 11 |Issue: 4 |Pages: 24
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781799862055|DOI: 10.4018/IJCVIP.2021100104
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MLA

Gupta, Monica, et al. "Analysis of Text Identification Techniques Using Scene Text and Optical Character Recognition." IJCVIP vol.11, no.4 2021: pp.39-62. http://doi.org/10.4018/IJCVIP.2021100104

APA

Gupta, M., Choudhary, A., & Parmar, J. (2021). Analysis of Text Identification Techniques Using Scene Text and Optical Character Recognition. International Journal of Computer Vision and Image Processing (IJCVIP), 11(4), 39-62. http://doi.org/10.4018/IJCVIP.2021100104

Chicago

Gupta, Monica, Alka Choudhary, and Jyotsna Parmar. "Analysis of Text Identification Techniques Using Scene Text and Optical Character Recognition," International Journal of Computer Vision and Image Processing (IJCVIP) 11, no.4: 39-62. http://doi.org/10.4018/IJCVIP.2021100104

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Abstract

In today's era, data in digitalized form is needed for faster processing and performing of all tasks. The best way to digitalize the documents is by extracting the text from them. This work of text extraction can be performed by various text identification tasks such as scene text recognition, optical character recognition, handwriting recognition, and much more. This paper presents, reviews, and analyses recent research expansion in the area of optical character recognition and scene text recognition based on various existing models such as convolutional neural network, long short-term memory, cognitive reading for image processing, maximally stable extreme regions, stroke width transformation, and achieved remarkable results up to 90.34% of F-score with benchmark datasets such as ICDAR 2013, ICDAR 2019, IIIT5k. The researchers have done outstanding work in the text recognition field. Yet, improvement in text detection in low-quality image performance is required, as text identification should not be limited to the input quality of the image.

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