EchoEase: Leveraging Python and Machine Learning for Enhanced Text-to-Speech Solutions for the Visually Impaired

EchoEase provides a new concept in Text-to-Speech (TTS) technology aimed at improving accessibility for blind people. Traditional TTS systems for the visually impaired frequently have optical character recognition (OCR) errors and speak robotically, making communication difficult. EchoEase, however, overcomes the limitation by combining powerful Python-based OCR with deep learning algorithmic algorithms. The technique uses Tesseract OCR for high-accuracy text extraction and improves the text with BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) before combining it in a deep-learning TTS engine to produce natural speech. According to the data acquired so far, EchoEase is a higher-performance TTS system. It has an OCR accuracy of up to $\mathbf{9 5. 8 \%}$ and creates speech with a naturalness score of $\mathbf{9. 2}$ far better than any other recognized system. User satisfaction is clearly higher. The results show that EchoEase has greatly outperformed traditional solutions like that on almost every metric. According to the results of the survey, EchoEase has the greatest satisfaction percentages for simplicity of use and customization.