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003 OCoLC;

005 20200422111539.0;

006 m o d ;

007 cr unu||||||||;

008 200313s2019 ncuab obm 000 0 eng d;

035 a| (Sirsi) o1144494950;

035 a| (OCoLC)1144494950;

049 a| EREE;

090 a| QA76.87;

100 1 a| Pala, Venkatesh Reddy,e| author.?| UNAUTHORIZED;

245 10 a| Text extraction from images using neural networks /c| by Venkatesh Reddy Pala.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2019.;

300 a| 94 pages :b| illustrations (some color, maps);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 9.446 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2019.;

500 a| Presented to the faculty of the Department of Computer Science;

500 a| Advisor: Venkat Gudivada;

500 a| Title from PDF t.p. (viewed April 8, 2020).;

520 3 a| Most western languages have witnessed the power of Artificial Intelligence (AI) in one other form. Primary fact for this achievement is due to the efforts of several researchers contributing to the field of computational linguistics. However, there are many languages in the World which has a great history and abundant literature but not many research activities due to many factors such as lack of motivation, non- availability of open-source corpora and so on. Telugu is one such language where there is a lack of efforts towards the digitization of language. The focus of this research is to extract text from the images to produce corpora for enabling computational linguistics and also to conserve the literature. Deep Learning with Neural Networks has proven solutions in the same domain.Optical Character Recognition is the solution adopted by western languages for digitization. However the same cannot be applied towards Telugu due to the complexity of scripts and the ambiguity in dialects. To address this issue, in this research we built a neural network system that can be adapted later for any such languages like Telugu. By adapting neural networks in this research we achieved an efficiency of 90 percent. Segmentation of characters is taken care by neural networks while we only specified the segmentation on word level. A comparative study of the system we developed and commercial API's is made and our system is proven to be more accurate.;

504 a| Includes bibliographical references.;

650 0 a| Neural networks (Computer science)=| ^A263002;

650 0 a| Programming languages (Electronic computers)=| ^A22846;

653 a| OCR;

653 a| Text Extraction;

653 a| Indic Scripts;

700 1 a| Gudivada, Venkat N.e| degree supervisor.=| ^A1308613;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/7630;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5266586;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5266586-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 3/13/2020x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5266586-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 3/13/2020x| ETDz| HERESOURCE;

Text extraction from images using neural networks / by Venkatesh Reddy Pala.

Author/creator	Pala, Venkatesh Reddy author.
Other author	Gudivada, Venkat N. degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2019.
Description	94 pages : illustrations (some color, maps)
Supplemental Content	Access via ScholarShip
Subjects	Neural networks (Computer science) Programming languages (Electronic computers)

Summary	Most western languages have witnessed the power of Artificial Intelligence (AI) in one other form. Primary fact for this achievement is due to the efforts of several researchers contributing to the field of computational linguistics. However, there are many languages in the World which has a great history and abundant literature but not many research activities due to many factors such as lack of motivation, non- availability of open-source corpora and so on. Telugu is one such language where there is a lack of efforts towards the digitization of language. The focus of this research is to extract text from the images to produce corpora for enabling computational linguistics and also to conserve the literature. Deep Learning with Neural Networks has proven solutions in the same domain.Optical Character Recognition is the solution adopted by western languages for digitization. However the same cannot be applied towards Telugu due to the complexity of scripts and the ambiguity in dialects. To address this issue, in this research we built a neural network system that can be adapted later for any such languages like Telugu. By adapting neural networks in this research we achieved an efficiency of 90 percent. Segmentation of characters is taken care by neural networks while we only specified the segmentation on word level. A comparative study of the system we developed and commercial API's is made and our system is proven to be more accurate.
General note	Presented to the faculty of the Department of Computer Science
General note	Advisor: Venkat Gudivada
General note	Title from PDF t.p. (viewed April 8, 2020).
Dissertation note	M.S. East Carolina University 2019.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Text extraction from images using neural networks / by Venkatesh Reddy Pala.

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