NDLI: Text Extraction from Low Quality Images using Deep Learning Techniques

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Artificial Intelligence (AI)

Communication Systems (ECC)

Computer Integrated Manufacturing (CIM)

Computer Science (CS)

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Text Extraction from Low Quality Images using Deep Learning Techniques

Industrial Instrumentation and Control (IIC)

Industrial Refrigeration and Cryogenic Engineering (IRCE)

Master of Computer Applications (MCA)

Structural Engineering & Construction Management (SECM)

Transportation Engineering (TE)

Urban Planning (UP)

Text Extraction from Low Quality Images using Deep Learning Techniques

Content Provider	TKM College of Engineering
Advisor	Nath, Aneesh G.
Author	Simon, Asha
Abstract	Today whatever look can see presence of digital image.Every day may groups like doctors, engineers, and students etc. release many images for their different needs.These images may contain both textual and non textual data. Text present in such images can provide meaningful information for content based repossession and many applications of computer vision like image understanding, reading number plates of moving vehicles and context retrieval for the investigation purposes.Text information found in these images may very different in fond size, style, alignment and orientation. In this way, it is very difficult to identify items with different characteristics and even more difficult to retrieve text if image is blurred or low resolution.There are many existing techniques for recover textual information from clear image. So in this paper, introduces a method to recover text from a blurred or low resolution image. The proposed technology is comprised of three main steps.(a) The deblurring process is applied to recover the clear image. (b) Extract text from images using text localization, segmentation and binarization techniques (c) Evaluation of proposed model. Input image deblurring achieved by using super resolution convolutional network. Text extraction can be achieved by using text extraction network. Here, use text detection to identify the text region on input image after that can find the exact position of text by using text localization and text segmentation separates the text from its background. Extensive experiments have been conducted on a synthetically generated dataset. Experimental results and analysis show that this system has better performance in terms of quantitative evaluation.
Language	English
Publisher Date	2022-09-01
Access Restriction	Open
Subject Keyword	Maximum Stable Extremal Regions Text-specific Hybrid Dictionary Binarization Techniques Wavelet Filter Low Resolution Image Digital Image
Content Type	Text
Resource Type	Thesis

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