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The advent of Optical Character Recognition (OCR) technology has revolutionized document handling for businesses, enabling the conversion of handwritten text into machine-readable content. This project explores Amazon Web Services Textract, a cutting-edge OCR service integrating advanced machine learning and deep learning models. Emphasis is placed on dissecting Textract's architecture, features, and practical applications, showcasing its proficiency in extracting text, forms, and tables from diverse document types such as scanned papers, PDFs, and images. Real-world examples demonstrate Textract's seamless integration with document management systems, facilitating data analysis and automation.Additionally, the article conducts a comprehensive evaluation of the efficiency and accuracy of three OCR solutions: Tesseract (English text), Amazon Textract (Arabic text), and Google DocumentAI (English text). Through a comparative analysis of their performance and identification of distinctions in handling common text imperfections, the study aids researchers in informed decision-making for OCR solutions aligned with specific research needs.Ultimately, the project underscores the utility and benefits of incorporating AWS Textract into OCR applications. It highlights how Textract enhances document processing efficiency, improves data accessibility, and ensures compliance adherence. Valuable insights are provided for organizations seeking to optimize document management workflows and leverage OCR capabilities through AWS Textract.

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Enhancing Text Extraction Using OCR with AWS Textract Service

  • 会议论文

  • 发表日期：

    2025年

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  • Aakash Bawasker,
  • Yash Dedania,
  • Vrushti Karia,
  • Nirav Bhatt,
  • Purvi Prajapati,
  • Nikita Bhatt

摘要

The advent of Optical Character Recognition (OCR) technology has revolutionized document handling for businesses, enabling the conversion of handwritten text into machine-readable content. This project explores Amazon Web Services Textract, a cutting-edge OCR service integrating advanced machine learning and deep learning models. Emphasis is placed on dissecting Textract's architecture, features, and practical applications, showcasing its proficiency in extracting text, forms, and tables from diverse document types such as scanned papers, PDFs, and images. Real-world examples demonstrate Textract's seamless integration with document management systems, facilitating data analysis and automation.Additionally, the article conducts a comprehensive evaluation of the efficiency and accuracy of three OCR solutions: Tesseract (English text), Amazon Textract (Arabic text), and Google DocumentAI (English text). Through a comparative analysis of their performance and identification of distinctions in handling common text imperfections, the study aids researchers in informed decision-making for OCR solutions aligned with specific research needs.Ultimately, the project underscores the utility and benefits of incorporating AWS Textract into OCR applications. It highlights how Textract enhances document processing efficiency, improves data accessibility, and ensures compliance adherence. Valuable insights are provided for organizations seeking to optimize document management workflows and leverage OCR capabilities through AWS Textract.