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This research introduces a comprehensive Text Recognition System that seamlessly integrates Optical Character Recognition (OCR) technology with an additional Text-to-Speech (TTS) feature. The primary objective is to convert printed or handwritten text from images into machine-readable text, offering enhanced user accessibility through audio output. The OCR module incorporates advanced algorithms to ensure precise text extraction, while the integrated TTS feature transforms the extracted text into natural and intelligible speech, enriching the overall user experience. Designed to address diverse applications such as document digitization, image-to-text conversion, and improved accessibility for visually impaired individuals, the proposed system employs sophisticated image processing techniques in the OCR component. This allows recognition of various fonts, styles, and languages, ensuring versatility and accuracy in text extraction. The TTS feature utilizes both prosodic and linguistic analysis to generate expressive and lifelike speech from the extracted text. Rigorous evaluation, considering factors such as accuracy, speed, and user experience, demonstrates the system’s proficiency, with preliminary results indicating its potential for widespread adoption in both professional and assistive technology domains. This research contributes significantly to advancing inclusive and efficient text recognition systems, promising improved human-computer interaction and accessibility within the digital landscape.

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Synergistic Fusion: OCR-Infused Text Recognition Framework with Intrinsic Text-to-Audio Integration

  • 会议论文

  • 发表日期：

    2026年

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  • Manish Sharma,
  • Anand Agrawal,
  • Devendra Nath Pathak,
  • Heena Singh,
  • Indra Bhooshan Sharma

摘要

This research introduces a comprehensive Text Recognition System that seamlessly integrates Optical Character Recognition (OCR) technology with an additional Text-to-Speech (TTS) feature. The primary objective is to convert printed or handwritten text from images into machine-readable text, offering enhanced user accessibility through audio output. The OCR module incorporates advanced algorithms to ensure precise text extraction, while the integrated TTS feature transforms the extracted text into natural and intelligible speech, enriching the overall user experience. Designed to address diverse applications such as document digitization, image-to-text conversion, and improved accessibility for visually impaired individuals, the proposed system employs sophisticated image processing techniques in the OCR component. This allows recognition of various fonts, styles, and languages, ensuring versatility and accuracy in text extraction. The TTS feature utilizes both prosodic and linguistic analysis to generate expressive and lifelike speech from the extracted text. Rigorous evaluation, considering factors such as accuracy, speed, and user experience, demonstrates the system’s proficiency, with preliminary results indicating its potential for widespread adoption in both professional and assistive technology domains. This research contributes significantly to advancing inclusive and efficient text recognition systems, promising improved human-computer interaction and accessibility within the digital landscape.