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040			_bEnglish. _cCvSU-CCAT Campus Library. _erda.
050			_aUM QA 76.9.S63 _bC33 2018
100			_aCabadsan, Mark Roger L., author _95602
245			_aRaspberry Pi-Based Multipurpose Optical Mark Recognition (OMR) Scanner / _cMarkRoger L. Cabadsan and Chrysolite A. Nocon.
260			_aRosario, Cavite : _bCavite State University-CCAT Campus, _c2018
300			_axiii, 70 leaves : _billustrations ; _c28 cm
500			_aDesign Project (BSCpE)--Cavite State University-CCAT Campus, 2018.
504			_aIncludes bibliographical references and appendices.
520			_aCABADSAN, MARK ROGER L., NOCON, CHRYSOLITE A. Raspberry Pi-Based Multipurpose Optical Mark Recognition (OMR) Scanner. Design Project. Department of Engineering. Cavite State University-Cavite College of Arts and Trades Campus, Rosario, Cavite. June 2018. Adviser: Engr. John Michael A. Dharma. Mr. Karlo Jose K. Nabablit. The study was conducted from August 2017 to June 2018 in order to design and develop a Raspberry Pi-based Multipurpose Optical Mark Recognition (OMR) Scanner for Cavite State University Cavite College of Arts and Trades (CCAT) that can automate the assessment of the Student Evaluation for Teachers (SET) and canvassing of Central Student Government (CSG) election votes. Specifically, the study aimed to: 1) design and develop a standalone Raspberry Pi-based OMR scanning device that is able of performing recognition, acquisition, storage and processing of all the necessary data or information and automatically assess and produce appropriate results; 2) evaluate the prototype technical performance in terms of accuracy, process time and over-all functionality; and 3) conduct a cost analysis of the research. Parameters measured are — 1) number of successful recognition per set, 2) number of accurate recognition per set and 3) process time per each cycle (seconds). These data were gathered to determine the probability of successful recognition, probability of producing an accurate result and the average processing time per cycle. The data were analyzed and the results showed that the Raspberry Pi-based Multipurpose Optical Mark Recognition (OMR) Scanner were able to automate the canvassing of CSG election votes and SET effectively and efficiently.
650			_aOptical Mark Recognition (OMR). _95603
650			_aRaspberry Pi-based. _95604
650			_aPerforming recognition. _95605
650			_aAcquisition. _95606
650			_aStorage and processing. _95607
650			_aCost analysis. _95484
700			_aNocon, Chrysolite A., author. _95608
700			_aDharma, John Michael A., adviser. _95496
700			_aNabablit, Karlo Jose E., critic. _95609
942			_2lcc _cT/M/D _hQA 76.9.S63 _iC33 2018
999			_c1769 _d1769