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Textractor : a mobile application using digital image processing / Dharissa Lyn P. Gumagay, Josephine S. Masamoc, and Ariel S. Acal.

By: Gumagay, Dharissa Lyn P., authorContributor(s): Masamoc, Josephine S., author | Acal, Ariel S., author | Estonilo, Christopher G., adviser | Nabablit, Karlo Jose E., technical criticMaterial type: TextTextPublisher: Rosario, Cavite : Cavite State University-CCAT Campus, 2019Description: xiii, 59 leaves : illustrations ; 28 cmSubject(s): Mobile applications | Character Recognition | Python (Computer Programming Language) | Scene text extraction | Mobile phone camera | TranslationLOC classification: UM QA 76.8 | G86 2019Summary: GUMAGAY, DHARISSA LYN P., MASAMOC, JOSEPHINE S., ACAL, ARIEL S. TexTractor: A Text Extractor Mobile Application Using Digital Image Processing. Cavite State University-CCAT, Rosario, Cavite. May 2019. Adviser: Prof. Christopher G. Estonilo. The study was conducted from August 2018 to April 2019 at the Department of Computer Studies of Cavite State University-CCAT. The general objective of the study was to extract the camera-captured printed characters into digital format using image processing. Specifically, the study aimed to 1) design and develop the mobile application using Phyton-Tesseract, which is capable of capturing the printed text using cellphone camera, separating the text from captured image aligned to it, editing and saving the text into pdf or text file format, and understand the saved text through text-to-speech of the cellphone; 2) evaluate the mobile application that comply with the ISO-IE 25010 software evaluation standards, and 3) prepare an implementation plan for the deployment of the mobile application in PlayStore and can be utilized by the users. The system analysis and design of TexTractor applied the rapid application development or RAD software development model which was composed of requirement analysis, user design (prototype, test, refine), construction, and cutover. The mobile application was evaluated by five (5) IT experts and (20) general end- user in the campus. It gained an average score of 4.50 and described as Very Satisfactory for the IT experts respondents. On the other hand, it gained a mean of 4.68 and described as Excellent for the end-users.
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Thesis/Manuscripts/Dissertations Thesis/Manuscripts/Dissertations Cavite State University - CCAT Campus
Thesis/Manuscript/Dissertation TH UM QA 76.8 G86 2019 (Browse shelf) 1 Available T0005211

An Undergraduate Thesis (BSCS) -- Cavite State University-CCAT Campus, 2019.

Includes bibliographical references and appendices.

GUMAGAY, DHARISSA LYN P., MASAMOC, JOSEPHINE S., ACAL, ARIEL S. TexTractor: A Text Extractor Mobile Application Using Digital Image Processing. Cavite State University-CCAT, Rosario, Cavite. May 2019. Adviser: Prof. Christopher G. Estonilo.

The study was conducted from August 2018 to April 2019 at the Department of Computer Studies of Cavite State University-CCAT. The general objective of the study was to extract the camera-captured printed characters into digital format using image processing. Specifically, the study aimed to 1) design and develop the mobile application using Phyton-Tesseract, which is capable of capturing the printed text using cellphone camera, separating the text from captured image aligned to it, editing and saving the text into pdf or text file format, and understand the saved text through text-to-speech of the cellphone; 2) evaluate the mobile application that comply with the ISO-IE 25010 software evaluation standards, and 3) prepare an implementation plan for the deployment of the mobile application in PlayStore and can be utilized by the users.

The system analysis and design of TexTractor applied the rapid application development or RAD software development model which was composed of requirement analysis, user design (prototype, test, refine), construction, and cutover.

The mobile application was evaluated by five (5) IT experts and (20) general end- user in the campus. It gained an average score of 4.50 and described as Very Satisfactory for the IT experts respondents. On the other hand, it gained a mean of 4.68 and described as Excellent for the end-users.

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