An Insight of Script Text Extraction Performance using Machine Learning Techniques
Shikha Chadha1, Sonu Mittal2,Vivek Singhal3

1Shikha Chadha*, Ph.D. Research Scholar, Jaipur National University, Rajasthan, India.
2Sonu Mittal, Associate Professor Department of Computer and System Sciences, Jaipur National University, Rajasthan, India.
3Vivek Singhal, Associate Professor, Department of Information &Technology, JSSATE, Noida, (U.P), India.

Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 2581-2588 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5224119119/2019©BEIESP | DOI: 10.35940/ijitee.A5224.119119
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Abstract: With the evolution of huge amount of ancient and modern text available in digital format, it is ascertain to mine for researchers, government, tourist and travelers visiting all over the world. However, it is very challenging and costly. Further, it takes a lot of effort and time for script text mining. Therefore, the study investigates various techniques for script text mining viz supervised and unsupervised techniques. Firstly, the study presents a survey for various kinds of techniques adopted by the users for extraction of text from image. It also delivers information about gaps involved in the various approaches. Furthermore, it incorporate the quantitative comparisons based among the study of various approaches and techniques for text extraction as well as script level comparison. The result inferred on the basis of the script comparison indicates that, the accuracy level of ancient script was found to be 5% lesser than modern script. Again, furthermore comparison has been done on standalone and hybrid machine (Combination of CNN and KNN) / deep learning techniques. The accuracy has been found to be lower(4%) in case of standalone techniques.
Keywords: Text Mining, Machine Learning, Deep Learning, Script, Image scene extraction, Convolutional Neural Network, K-Nearest Neighbors.
Scope of the Article: Machine Learning