Abstract: Handwritten text recognition and conversion to speech is a complex task that involves multiple stages and technologies. The process begins with image processing, where the handwritten text is captured and pre-processed to enhance its quality and remove any noise. The next step is to perform optical character recognition (OCR), which involves recognizing individual characters in the text and converting them into a digital form that can be processed by a computer. Once the text has been digitized, it is processed by natural language processing (NLP) algorithms to identify and extract relevant information, such as dates, names, and numbers. The final step is to convert the digitized text into speech using text-to-speech (TTS) technology. This involves synthesizing a spoken language representation of the text, typically using machine learning algorithms to model the pronunciation and rhythm of human speech. Overall, handwritten text recognition and conversion to speech is a challenging task that requires a combination of image processing, OCR, NLP, and TTS technologies. However, advances in these fields have made it possible to create systems that can accurately recognize and convert handwritten text into speech with high levels of accuracy