Development of PEREA: A Raspberry Pi-Based Personal Reading Assistant for the Blind Persons / Antonio J. Gualin Jr. and Nathaniel E. Luna.
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TextPublisher: Rosario, Cavite : Cavite State University-CCAT Campus, 2018Description: xiv, 44 leaves : illustrations ; 28 cmSubject(s): Image processing -- Digital techniques | Raspberry pi-based reader for blind | Computer vision | Raspberry pi (Microcontroller)LOC classification: UM TA 1637 | G83 2018Summary: GUALIN, ANTONIO JR. J. LUNA, NATHANIEL E. Development of PEREA: A Raspberry Pi-Based Personal Reading Assistant for the Blind Persons. Design Project. Department of Engineering. Cavite State University - Cavite College of Arts and Trades Campus, Rosario, Cavite. June 2018. Adviser: Kenneth J. Enrico. Technical critic: Allen Paul K. Aclan.
The study was conducted from August 2017 to May 2018 to develop PEREA, a raspberry pi-based personal reading assistant that can read a full text page of printed book and convert it in audible form for the blind persons. Specifically the study aimed to 1.) design and construct personal reading assistant using raspberry pi, pi camera and other materials: 2.) perform image analysis on scanned documents in order to convert texts files to audible format: 3.) evaluate the reading performance of the system; and 4.) conduct cost analysis of the project.
PEREA captures a full text page of the printed material, clean image through image processing, extract text from the cleaned image, and converts text into speech or audio. The developers evaluated PEREA in terms of font style, line spacing, font size, and font color. Punctuation marks and basic formatting were also evaluated based on how it will be read by the device.
Based on the results of the evaluation, there are some formats that are more accurate than other. Average processing time is around 120 seconds. This revealed some limitations of the system and arises some recommendations for the improvement of the study.
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Cavite State University - CCAT Campus | Thesis/Manuscript/Dissertation | TH | UM TA 1637 G83 2018 (Browse shelf) | 1 copy | Available | T0004210 |
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| UM TA 1637 A24 2018 Facial Recognition System for Car Ignition using OpenCV, DLIB Framework and Raspberry Pi 3 Model B / | UM TA 1637 A43 2019 Coffee Berry Grader and Ripeness Detector / | UM TA 1637 C66 2019 Real Time Obstacle Detection for Visual Impaired Individuals / | UM TA 1637 G83 2018 Development of PEREA: A Raspberry Pi-Based Personal Reading Assistant for the Blind Persons / | UM TA 1637 M88 2019 MediLeaf : a Herbal Plant Classification System using Image Processing / | UM TA165 D45 2018 Development of Electronics Mousetrap / | UM TA 165 D56 2016 The development of automated dissolved oxygen monitoring system / |
Design Project (BSCpE)--Cavite State University-CCAT Campus, 2018.
Includes bibliographical references and appendices.
GUALIN, ANTONIO JR. J. LUNA, NATHANIEL E. Development of PEREA: A Raspberry Pi-Based Personal Reading Assistant for the Blind Persons. Design Project. Department of Engineering. Cavite State University - Cavite College of Arts and Trades Campus, Rosario, Cavite. June 2018. Adviser: Kenneth J. Enrico. Technical critic: Allen Paul K. Aclan.
The study was conducted from August 2017 to May 2018 to develop PEREA, a raspberry pi-based personal reading assistant that can read a full text page of printed book and convert it in audible form for the blind persons. Specifically the study aimed to 1.) design and construct personal reading assistant using raspberry pi, pi camera and other materials: 2.) perform image analysis on scanned documents in order to convert texts files to audible format: 3.) evaluate the reading performance of the system; and 4.) conduct cost analysis of the project.
PEREA captures a full text page of the printed material, clean image through image processing, extract text from the cleaned image, and converts text into speech or audio. The developers evaluated PEREA in terms of font style, line spacing, font size, and font color. Punctuation marks and basic formatting were also evaluated based on how it will be read by the device.
Based on the results of the evaluation, there are some formats that are more accurate than other. Average processing time is around 120 seconds. This revealed some limitations of the system and arises some recommendations for the improvement of the study.
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