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What are the best tools and techniques for getting started with extracting text from PDFs?
PDFs can be divided into three types: text-based, image-based, and hybrid, each requiring different extraction methods.
The Portable Document Format (PDF) was created in the 1990s by Adobe as a way to share documents independent of device or operating system.
Optical Character Recognition (OCR) technology is used to extract text from image-based PDFs by recognizing patterns and shapes.
The quality of extracted text depends on the quality of the original PDF, making high-quality scans essential for accurate extraction.
PDF-to-text converters use Natural Language Processing (NLP) algorithms to improve the accuracy of extracted text.
AI-powered tools, like Instabase's Converse app, utilize machine learning to extract text from PDFs with high accuracy.
The PyPDF2 library in Python is commonly used for extracting text from PDFs, but it can be limited in its ability to handle complex layouts.
PDFminer, a Python library, provides a more comprehensive solution for extracting text and layout analysis.
The PDF format supports various compression algorithms, including JPEG, LZW, and Flate, which can affect text extraction.
Smallpdf's online OCR tool uses machine learning algorithms to extract text from PDFs in seconds, supporting Mac, Windows, and Linux devices.
Adobe Acrobat's OCR feature uses advanced image processing and machine learning to extract text from scanned documents.
The accuracy of extracted text can be improved by preprocessing the PDF, handling formatting and layouts before extraction.
Automating the process of extracting text from PDFs can be challenging due to variations in PDF structure and content.
PDF-to-text conversion tools can be categorized into three types: online tools, offline software, and programming libraries.
Extracting text from PDFs is a common task in Natural Language Processing (NLP) and Information Retrieval applications, used in various industries such as law, healthcare, and finance.
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