SBC-Based Object and Text Recognition Wearable System using Convolutional Neural Network with Deep Learning Algorithm
Melchiezedhieck J. Bongao1, Arvin F. Almadin2, Christian L. Falla3, Juan Carlo F. Greganda4, Steven Valentino E. Arellano5, Phillip Amir M. Esguerra6
1Melchiezhedhieck J. Bongao*, Computer Engineering Department, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna Philippines.
2Arvin F. Almadin, Computer Engineering Department, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
3Christian L. Falla, Computer Engineering Department, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
4Juan Carlo F. Greganda, Computer Engineering Department, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
5Steven Valentino E. Arellano, Computer Engineering Department, College of Engineering and Graduate School, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
6Phillip Amir M. Esguerra, Electronics Engineering Department, College of Engineering and Graduate School, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
Manuscript received on September 17, 2021. | Revised Manuscript received on September 24, 2021. | Manuscript published on September 30, 2021. | PP: 198-205 | Volume-10 Issue-3, September 2021. | Retrieval Number: 100.1/ijrte.C64740910321 | DOI: 10.35940/ijrte.C6474.0910321
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This Raspberry Single-Board Computer-Based Object and Text Real-time Recognition Wearable Device using Convolutional Neural Network through Tensor Flow 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.
Keywords: Visually Impaired, Real-time Object and Text Recognition, Convolutional Neural Network, Deep Learning