apache/tika

Features

• Content Extraction - Provides a unified interface for retrieving raw text and metadata from a vast variety of document types.
• MIME Type Detection Engines - Determines file formats by analyzing magic bytes, filenames, and metadata using a sequence of prioritized detectors.
• Document Text Extractors - Converts complex binary files like PDFs and Office documents into plain text or XHTML for downstream processing.
• Stream-Based Parsing - Implements event-driven parsing of large documents to extract text while minimizing memory consumption.
• General Metadata Engines - Implements a framework for retrieving embedded properties and descriptive attributes from diverse media and document formats.
• Content Analysis Toolkits - Ships a comprehensive Java library for detecting and extracting metadata and text from thousands of different file formats.
• Content Type Detection - Automates the identification of media types by analyzing byte patterns, filenames, and metadata.
• Document Parsing Engines - Converts unstructured documents from complex formats into structured plain text or XHTML.
• Media Metadata Extraction - Retrieves descriptive attributes and system metadata from images, audio, video, and scientific formats.
• MIME Type Detection - Identifies the format and media type of a file by analyzing byte patterns and file metadata.
• MIME Type Detectors - Includes a specialized detector that identifies document formats using magic bytes and metadata to determine the correct media type.
• Digital Asset Metadata Retrieval - Pulls descriptive attributes and system properties from images, audio, video, and scientific file formats.
• Magic Byte File Identification - Identifies file types by analyzing header magic bytes to determine the correct parser for extraction.
• Optical Character Recognition - Integrates with external OCR tools to extract text from image files and scanned PDFs.
• Named Entity Recognition - Extracts specific named entities such as people and organizations from text using external recognition services.
• Language Identification - Analyzes text within documents to identify the natural language used when metadata is missing.
• Natural Language Processing Analysis - Integrates OCR and language detection to perform linguistic analysis and process text from images.
• Memory-Efficient Chunking - Processes extracted content in small segments via custom handlers to maintain a low memory footprint.
• Sentiment Analysis Tools - Classifies the emotional tone of documents by processing text through integrated natural language analysis tools.
• Custom Extractor Implementations - Provides interfaces for implementing custom logic to extract text and metadata from unsupported file formats.
• Visual Content Analysis - Identifies objects and visual elements within images and videos by integrating with external recognition frameworks.
• Email Text Extraction - Extracts individual messages and attachments from mailbox formats such as mbox, PST, and MSG.
• OCR Integration Gateways - Provides an interface for connecting to external text recognition tools to extract text from image files.
• Multi-Format Archive Extraction Commands - Decompresses various packaging formats like Zip, Tar, and RAR to extract nested document streams.
• Email Archive Processing - Extracts individual messages and embedded attachments from mailbox formats like PST and mbox.
• Parsing Behavior Configurations - Enables control over parser selection and priority via configuration files to override default extraction behaviors.
• Container Format Inspection - Analyzes files wrapped in common container formats to determine the specific document type residing inside.
• Detection Pipeline Configurations - Allows users to define active detectors and their execution sequence to determine the MIME type of a document.
• Structural Event Streaming - Converts documents into a stream of events to preserve structural elements without loading the full file into memory.
• Memory-Efficient Streaming - Processes extracted text in small segments via custom handlers to minimize memory usage for large documents.
• Recursive Document Unpacking - Decompresses nested container formats to extract and parse embedded document streams and attachments.
• Embedded File Extraction - Retrieves and saves embedded files and attachments found within documents to a local directory.
• External Process Delegation - Delegates complex tasks like OCR and translation to external third-party binary tools.
• Recursive Batch Processing - Automates the extraction of content and metadata for all files within a directory using multi-threading.
• Encoding Extraction - Automatically determines the character encoding of plain text documents to ensure accurate extraction.
• Parser Mapping Interfaces - Maps detected MIME types to specialized parser classes through a unified interface for consistent text extraction.

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