1. What is Apache Tika?
Apache Tika - a content analysis toolkit
The Apache Tika™ toolkit detects and extracts metadata and text from over a thousand different file types (such as PPT, XLS, and PDF). All of these file types can be parsed through a single interface, making Tika useful for search engine indexing, content analysis, translation, and much more. You can find the latest release on the download page. Please see the Getting Started page for more information on how to start using Tika.
The Parser and Detector pages describe the main interfaces of Tika and how they work.
If you're interested in contributing to Tika, please see the Contributing page or send an email to the Tika development list.
Tika is a project of the Apache Software Foundation, and was formerly a subproject of Apache Lucene.
Apache Tika library for file type detection and extraction from file content in various formats.
Internally, Tika uses existing various file parsers and document type detection techniques to detect and extract data.
Using Tika, one can develop general purpose detectors and content extraction to different types of files such as spreadsheets, text files, images, PDF files and even multimedia input formats, to a certain extent extract structured text and metadata.
Tika provides a common API for parsing different file formats. It uses 83 existing professional parser libraries for each document type.
All these parser libraries are packaged according to a single interface called the Parser interface.
2. Why use Tika?
According to the website fileext.com, there are about 15,000 to 51K content types, and this number is increasing day by day. Data is stored in different formats such as text documents, excel sheets, PDFs, images and multimedia files, just to name a few. Therefore, applications such as search engines and content management systems require additional support for easily extracting data from these document types. Apache Tika achieves this by providing a common API to detect and extract data services in multiple file formats.
3. Apache Tika application
There are various applications using Apache Tika. Here, we will discuss several prominent applications that rely heavily on Apache Tika.
1) Search engine
Tika is widely used for developing the textual content of digital documents indexed by search engines.
A search engine is an information processing system for searching web page information and indexing files.
Crawlers are an important part of a search engine that obtains files that are indexed using some indexing techniques through web scraping. Thereafter, crawlers deliver these index files to extract components.
The responsibility of the extraction component is to extract the text and metadata in the document. The content and metadata extracted in this way are very useful to search engines. This extraction component is included in Tika.
The extracted content is then passed to an indexer that uses it to build a search index for search engines. In addition, the same is true for the content that the search engine extracts using many other means.
2) Search Engine document analysis
In the field of artificial intelligence, there are certain tools to automatically analyze documents at the semantic level and extract various data from them.
In this application, the documents are classified based on salient aspects of the extracted content of the document.
These tools use Tika for content extraction to analyze files ranging from plain text to different digital documents.
3) Digital asset management
Some organizations manage their digital assets such as photos, e-books, drawings, music and videos using a special application called Digital Asset Management (DAM).
Such applications take the help of file type detectors and metadata extractors to classify various files.
4) Content Analysis
Sites like Amazon recommend just publishing their site content to individual users based on their interests. To do this, these sites follow machine learning techniques, or take the help of social media sites like Facebook, to extract the required information such as likes and user interests. This collected information will be in the form of HTML tags or other formats that require additional content type detection and extraction.
For a document, content analysis, we have technologies that implement machine learning techniques such as UIMA and Mahout. These techniques are useful in clustering and analyzing data in files.
Apache Mahout is a framework that provides ML algorithms based on Apache Hadoop - a cloud computing platform. Mahout provides the architecture for one of the following clustering and filtering techniques. Following this architecture, programmers can write their own ML algorithms that generate recommendations by taking various combinations of text and metadata. To provide input to these algorithms, recent versions of Mahout use Tika to extract the text and metadata of binary content.
Apache UIMA analyzes and processes various programming languages and generates UIMA annotations. Internally, it uses Tika annotators to extract text and metadata from documents.
4. Tika application layer architecture
Application programmers can easily integrate Tika in their applications. Tika provides a command line interface and graphical user interface to make it more user-friendly.
There are four important modules that make up the Tika architecture. The architecture of Tika's four modules:
Language Detection Mechanism
Whenever a text file is passed to Tika, it will detect the language in it. It accepts annotated files without a language and adds metadata information to the file by detecting the language.
To support language identification, Tika has a class called language identifiers in the package org.apache.tika.language and the language identification database contains algorithms for language detection from a given text. Tika uses N-gram algorithm language detection internally.
MIME detection mechanism
Tika can detect document types according to the MIME standard. Tika's default MIME type detection uses org.apache.tika.mime.mimeTypes. It uses the org.apache.tika.detect.Detector interface for most content type detection.
Internally Tika uses a variety of techniques such as file matching substitution, content-type hinting, magic bytes, character encoding, and a few others.
Parser interface
The org.apache.tika.parser parser interface is the main interface for Tika to parse documents. This interface extracts text and metadata from documents and summarizes it for external users willing to write parser plugins.
Using different concrete parser classes, specifically for each document type, Tika supports a large number of file formats. Concrete classes of these formats provide support for different file formats, either by implementing a logic parser directly or using an external parser library.
Tika Facade
Using the Tika facade class is the easiest and most straightforward way to call Tika from Java, and also follows the facade design pattern. Facade classes can be found in the Tika API's org.apache.tika package Tika.
By implementing basic use cases, Tika acts as a proxy for the facade. It abstracts the underlying complexity of the Tika library, such as MIME detection mechanism, parser interface and language detection mechanism, and provides users with a simple interface to use.
5. Features of Tika
Unified Parser Interface: Tika encapsulates third-party parser libraries in a single parser interface. Due to this feature, the user is relieved from the burden of choosing the appropriate parser library and using it, depending on the file type encountered.
Low memory footprint: Tika therefore consumes less memory resources and is also easy to embed in Java applications. You can also use the Tika platform to run the application with less resources like a mobile PDA.
Fast processing: Content detection and extraction from application links can be expected.
Flexible metadata: Tika understands the metadata model all of which are used to describe files.
Parser integration: Tika can use various parser libraries for each file type in a single application.
MIME Type Detection: Tika can detect and extract content from all media types included in the MIME standard.
Language Detection: Tika includes language recognition, so it can be used in documents based on language types on a multilingual website.
6. The function of Tika
Tika supports multiple functions:
Document type detection
Content extraction
Metadata extraction
language detection