Authors :
Kiran Gurav; Neel Joshi; Nida Desai; Shruti Ghorpade
Volume/Issue :
Volume 9 - 2024, Issue 8 - August
Google Scholar :
https://tinyurl.com/3bu4383a
Scribd :
https://tinyurl.com/396zcrpr
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24AUG491
Abstract :
This research introduces a novel application of
the raspberry pi platform in the development of an
integrated text reader and object detection system. The
system aims to assist visually impaired individuals and
enhance overall accessibility for users with diverse needs.
Leveraging the power of computer vision and edge
computing, the proposed solution employs a raspberry pi,
a low-cost, compact, and energy-efficient single-board
computer.
The text reader component utilizes optical character
recognition (ocr) algorithms to convert printed or
handwritten text into digital format. This feature enables
users to obtain audible information from text-based
sources, such as books, documents, or signage. The
implementation of real-time processing on the raspberry
pi ensures prompt and on-device text recognition,
reducing dependence on external servers and enhancing
privacy.
Furthermore, the system incorporates an object
detection module to identify and describe objects in the
user's surroundings. This functionality enhances the
user's spatial awareness by providing audio cues about
the presence and characteristics of objects. The object
detection system employs a pre-trained deep neural
network, making it adaptable to various object
recognition tasks.
The entire system is designed with a user-friendly
interface that facilitates interaction through speech and
audio feedback. Additionally, the portability and
affordability of the raspberry pi make the solution
accessible to a broad user base.
Preliminary testing of the prototype has shown
promising results in terms of accuracy, speed, and
usability. The raspberry pi-based text reader & object
detection system holds potential for improving the quality
of life for individuals with visual impairments and can
contribute to the advancement of assistive technologies
with its cost-effective and scalable approach. Future work
involves refining the system, expanding the object
detection capabilities, and conducting extensive user trials
to gather feedback for further improvements.
References :
- Wikipedia raspberry pi: https://en.wikipedia.org/wiki/raspberry_pi
- S. Koley and r. Mishra voice operated outdoor navigation system for visually impaired persons international journal of engineering trends and technology, vol.3, issue 2,2012.
- S. Dhambre and a. Sakare smart stick for blind: obstacle detection, artificial vision and real-time assistance via gps,”2nd international conference on information and communication technology (ncict)2011.
- Ajantha devi and santhosh baboo`s “embedded optical character recognition on tamil text image using raspberry pi”, international journal of computer science trends and technology (ijcst), volume. 2, issue 4, july-august 2014.
- Vasanthi. G and ramesh babu y india. Vision based assistive system for label detection with voice output, department of ece, dmi college of engineering, chennai, jan 2014.
- Vikram shirol, abhijit m, savitri a et al. “drashti an android reading aid” international journal of computer science and information technologies vol.6 (july 2015) Chatgpt for code
- Raspberry pi os https://www.raspberrypi.com/software/operating-systems/
- Open-cv download https://opencv.org/releases/
This research introduces a novel application of
the raspberry pi platform in the development of an
integrated text reader and object detection system. The
system aims to assist visually impaired individuals and
enhance overall accessibility for users with diverse needs.
Leveraging the power of computer vision and edge
computing, the proposed solution employs a raspberry pi,
a low-cost, compact, and energy-efficient single-board
computer.
The text reader component utilizes optical character
recognition (ocr) algorithms to convert printed or
handwritten text into digital format. This feature enables
users to obtain audible information from text-based
sources, such as books, documents, or signage. The
implementation of real-time processing on the raspberry
pi ensures prompt and on-device text recognition,
reducing dependence on external servers and enhancing
privacy.
Furthermore, the system incorporates an object
detection module to identify and describe objects in the
user's surroundings. This functionality enhances the
user's spatial awareness by providing audio cues about
the presence and characteristics of objects. The object
detection system employs a pre-trained deep neural
network, making it adaptable to various object
recognition tasks.
The entire system is designed with a user-friendly
interface that facilitates interaction through speech and
audio feedback. Additionally, the portability and
affordability of the raspberry pi make the solution
accessible to a broad user base.
Preliminary testing of the prototype has shown
promising results in terms of accuracy, speed, and
usability. The raspberry pi-based text reader & object
detection system holds potential for improving the quality
of life for individuals with visual impairments and can
contribute to the advancement of assistive technologies
with its cost-effective and scalable approach. Future work
involves refining the system, expanding the object
detection capabilities, and conducting extensive user trials
to gather feedback for further improvements.
