Image Text to Speech Conversion in Desired Language and Summarization with Raspberry PI
Authors: Patel H Akshay , R Mourya , M Moulya , K B Vismitha , HM Darshan 
Published: December 2024

In recent years, the integration of artificial intelligence
and embedded systems has gained significant traction,
enabling the development of efficient and user-friendly
solutions. ourproject focuses on building a system
capable of extracting text from images, converting the
extracted content into speech in a desired language, and
providing concise summaries, all powered by a
Raspberry Pi. The system employs Optical Character
Recognition (OCR) for text extraction, a text-to-speech
engine for audio synthesis, and natural language
processing techniques for summarization. Designed
with accessibility and versatility in mind, the system
can assist individuals with visual impairments, language
barriers, or those seeking quick comprehension of
extensive information. By leveraging the computational
efficiency and affordability of the Raspberry Pi, the

proposed solution aims to deliver a portable, cost-
effective, and scalable platform for text and audio

processing applications.
Advancements in embedded systems and artificial
intelligence have enabled the development of
innovative solutions that enhance accessibility and
improve user experience. ourproject presents a
multifunctional system designed to process images,

extract text, convert the text into speech in the desired
language, and summarize the content, utilizing the
Raspberry Pi as a cost-effective and portable platform.
The system integrates Optical Character Recognition
(OCR) for accurately identifying text within images, a
text-to-speech synthesis engine for converting text to
audio output, and natural language processing (NLP)
algorithms to generate concise and meaningful
summaries.
Our approach offers significant benefits, including
aiding visually impaired individuals, breaking down
language barriers, and streamlining content
consumption for users seeking quick comprehension of
extensive material. The choice of the Raspberry Pi
ensures affordability, energy efficiency, and scalability,
making it suitable for various real-world applications.
The proposed solution incorporates

multilingual support, enabling audio output in a
language of the user’s choice, thereby enhancing its
versatility.
The project also explores optimization techniques to
ensure real-time performance, despite the hardware
constraints of the Raspberry Pi. Applications of

INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (IJSREM)
VOLUME: 08 ISSUE: 12 | DEC - 2024 SJIF RATING: 8.448 ISSN: 2582-3930

© 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM39818 | Page 2
oursystem span education, accessibility tools, document

digitization, and language learning. By leveraging open-
source tools and libraries, the project underscores the

potential of embedded systems in democratizing
technology for a broader audience.
Keywords
Artificial Intelligence (AI),Embedded
Systems,Raspberry Pi,Optical Character Recognition
(OCR),Text-to-Speech (TTS),Natural Language
Processing (NLP),Multilingual
Support,Accessibility,Real-timeOptimization,Image
Processing,ContentSummarization,Cost-effective
Solutions,Scalable Systems,Open-source
Tools,Document Digitization,Language Learning