Abstract—The study introduces Teacher SCARLET: a smart classroom assistance for rich learning environment tool. The proponents developed a blackboard-handwritten character recognition system for a biology lesson data extraction. Its
features include handwritten-blackboard character recognition, multimedia approach of learning, printable documents and ready-to-go presentation materials for teaching. The study used Quasi-experimental method in determining the degree of accuracy of recognition for the developed software through experiment paper. The developed software was evaluated by two (2) groups of respondents comprising of 3rd year high school students as student respondents and science teachers as
experts using survey questionnaires. The questions were categorized by accuracy, user-friendliness, functionality and appropriateness. After conducting the study, it showed that the developed system can be used by the students and the teachers
to support the learning process within the classroom.
Index Terms—Artificial intelligence, e-learning, image analysis and processing, pattern recognition.
The authors are with the College of Computer and Information Sciences, Polytechnic University of the Philippines, Sta. Mesa, Philippines (e-mails:
jophet.comia@gmail.com; lsushmita31@gmail.com;
cj_fandino@yahoo.com; gregoriokevind@gmail.com;
rmmontaril@yahoo.com; bennycomendador@yahoo.com).
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Cite:Jophet Comia, Lou Sushmita Mae Bernardino, Christian Joseph Fandiño, Kevin Drexler Gregorio, Ranil Montaril, and Benilda Eleonor Comendador, "Teacher SCARLET: An Application of Artificial Neural Networks in Off-Line Blackboard-Handwritten Character Recognition for Biology Lesson Data Extraction," Journal of Advances in Computer Networks vol. 2, no. 3, pp. 193-197, 2014.
