Computational Intelligence and Machine Learning
Reportlab Based Transcription Transformer
Authors: P. Anjaneyulu, D. Priyanka, Y. Manaswini, G. Sanjana, B. Sai Haneesha, B. Manideep and M. Harshitha
Publishing Date: 23-04-2025
ISBN: 978-81-975670-5-6
Abstract
In an era where digital communication is paramount, harnessing the power of voice data and converting it into text and structured documents is unprocurable. This system excels at converting Audio files into text and seamlessly transforming text into PDF documents, with the overarching goal of providing a seamless and comprehensive solution for converting spoken words into written text and subsequently crafting polished PDF documents. The core of this lies in implementing advanced speech recognition algorithms to transcribe spoken words accurately. Leveraging machine learning and natural language processing techniques, it achieves high precision in converting audio input into text format. Furthermore, it offers flexibility by accommodating language and accents. Once the speech is accurately transcribed, the system seamlessly integrates with PDF generation capabilities. It assembles the transcribed text into well-structured PDF documents, complete with formatting options. The significance can hold immense potential to revolutionize transcription services, offering a groundbreaking solution that drastically reduces the time and effort required by professionals in various domains. It improves accessibility for differently-abled individuals in healthcare only aids individuals with hearing impairments. and education in Lecture Transcription and Note- Taking. Its user-friendly interface makes it valuable in many contexts.
Keywords
Long Short-Term Memory, Convolutional Neural Network, Transformers, ReportLab, FPDF(F-PDF).
Cite as
P. Anjaneyulu, D. Priyanka, Y. Manaswini, G. Sanjana, B. Sai Haneesha, B. Manideep and M. Harshitha, "Reportlab Based Transcription Transformer", In: Sandeep Kumar and Kavita Sharma (eds), Computational Intelligence and Machine Learning, SCRS, India, 2025, pp. 85-96. https://doi.org/10.56155/978-81-975670-5-6-7