Abstract
OCR makes machines to recognize text automatically. It not only saves retrieval times but also helps in digitalization of important documents. In this paper different techniques are used for converting textual matter from an paper document or an image into machine readable form (OCR text output) and then into audio output using gTTS (Google Text-to Speech, a Python library and CLI tool to interface with Google Translates text-to-speech API). In proposed model, the content writes spoken mp3 data to a file, a file-like object (byte string) for further audio manipulation with flexible pre-processing and tokenizing. The approach results into a graspable and comprehensible text output & then fed into audio output for inference with 99 % accuracy. This paper outlines the stages of development, the major challenges with some interesting result comparison with different state of approaches.
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