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Processing large text streams in small segments to maintain a low memory footprint.
Distinct from Text Segmentation: Focuses on memory optimization during streaming rather than linguistic segmentation boundaries.
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Tika is a content analysis toolkit and Java library designed for detecting and extracting metadata and text from thousands of different file types. It functions as a universal document text extractor and metadata extraction engine, converting complex files into plain text or XHTML. The system employs a specialized MIME type detector that identifies document formats using magic bytes and metadata to determine the correct parser. It serves as an OCR integration gateway, connecting to external text recognition tools to extract content from image files. The project covers a broad range of extrac
Processes extracted text in small segments via custom handlers to minimize memory usage for large documents.