Text extraction is the process of automatically pulling structured text content from unstructured or semi-structured documents like PDFs, images, emails, or web pages.
Common formats include PDF, DOC/DOCX, TXT, RTF, HTML, XML, JPEG, PNG, TIFF, and scanned documents through OCR technology.
Key applications include data mining, document processing, content analysis, information retrieval, automated data entry, and digital document management systems.
OCR (Optical Character Recognition) analyzes images of text, identifies character patterns, and converts them into machine-readable text using pattern recognition algorithms.
Major challenges include handling complex layouts, maintaining text formatting, dealing with multiple languages, processing poor quality images, and managing varied document structures.
Yes, modern text extraction tools support multiple languages and scripts, though accuracy may vary depending on the language and character set.
Accuracy typically ranges from 95-99% for high-quality documents in common languages, but can be lower for poor quality sources or complex layouts.
Quality can be improved through pre-processing steps, using high-resolution sources, applying noise reduction, and implementing post-extraction validation.
Popular tools include Adobe Acrobat, ABBYY FineReader, Tesseract OCR, Amazon Textract, and Google Cloud Vision API.
Extracted text can be stored in various formats including plain text, structured XML/JSON, databases, or integrated directly into document management systems.