TEXT TO SPEECH CONVERSION

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 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.