SBC Based Object and Text Recognition Wearable System u sing Convolutional Neural Network with Deep Learning Algorithm

-22b9b34417bc6092a74-6cc4 20210924075036465 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 09 30 2021 10 3 10.35940/ijrte.10.3 https://www.ijrte.org/download/volume-10-issue-3/ SBC Based Object and Text Recognition Wearable System u sing Convolutional Neural Network with Deep Learning Algorithm Computer Engineering Department, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna Philippines. Melchiezhedhieck J. Bongao Arvin F. Almadin Computer Engineering Department, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines. Christian L. Falla Computer Engineering Department, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines. Juan Carlo F. Greganda Computer Engineering Department, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines. Steven Valentino E. Arellano Computer Engineering Department, College of Engineering and Graduate School, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines. Phillip Amir M. Esguerra Electronics Engineering Department, College of Engineering and Graduate School, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines. This Raspberry Single-Board Computer-Based Object and Text Real-time Recognition Wearable Device using Convolutional Neural Network through TensorFlow Deep Learning, Python and C++ programming languages, and SQLite database application, which detect stationary objects, road signs and Philippine (PHP) money bills, and recognized texts through camera and translate it to audible outputs such as English and Filipino languages. Moreover, the system has a battery notification status using an Arduino microcontroller unit. It also has a switch for object detection mode, text recognition mode, and battery status report mode. This could fulfill the incapability of visually impaired in identifying of objects and the lack of reading ability as well as reducing the assistance that visually impaired needs. Descriptive quantitative research, Waterfall System Development Life Cycle and Evolutionary Prototyping Models were used as the methodologies of this study. Visually impaired persons and the Persons with Disability Affairs Office of the City Government of Biñan, Laguna, Philippines served as the main respondents of the survey conducted. Obtained results stipulated that the object detection, text recognition, and its attributes were accurate and reliable, which gives a significant distinction from the current system to detect objects and recognize printed texts for the visually impaired people. 09 30 2021 198 205 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.C6474.0910321 https://www.ijrte.org/wp-content/uploads/papers/v10i3/C64740910321.pdf