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Image Text detection Text recognition
Text Summarization
Text summary is a technique for shortening long passages of text into manageable pieces of information. The goal is to
develop a logical and fluent summary that only includes the document's major ideas. The technique has proven to be
crucial in swiftly and accurately summarising large texts, which would be costly and time-consuming if done manually.
Before producing the requisite summary texts, machine learning models are normally trained to comprehend documents
and condense the useful information. Text
summarization shortens reading time, speeds up the
research process, and expands the quantity of NLP
Extracted
information that can be stored in a given space. For Full Summary
example, companies involved in writing/producing Article
e-books, and blogs, use summarising to break down
their information and make it shareable on social
media sites like Twitter and Facebook. Companies
reuse current content more effectively as a result of this.
Information Extraction
The automatic retrieval of specific information relating to a given topic from a document or multiple documents is
known as information extraction (IE). Information extraction tools help extract data from text documents, databases,
webpages and other sources. IE can extract data from a machine-readable text that is unstructured, semi-structured, or
structured. For example, finance, medical chains, transportation, and construction companies deal with vast quantities of
documents daily. Everyone on the team can search, update, and analyse significant transactions and data across business
processes using NLP information extraction techniques on documents. For example, the process of KYC verification is
done by obtaining ethical information from the customer's identity documents.
Speech Processing
Speech processing uses NLP so that smart devices like smartphones can interact with users through verbal language.
One of the best-known examples of speech recognition technology on a mobile device is Apple's Siri speech recognition
service. Siri uses built-in microphones to recognize speech (such as commands, questions) and Automatic Speech
Recognition (ASR) to convert them to text. The software then translates the transcribed text into "parsed text" and then
evaluates it locally on the device. If the request cannot be processed on the device, Siri communicates with the cloud
servers to help process the request. Once the command is executed (for example, to perform an internet search or
provide directions to a location), Siri will present the information and/or return a verbal response to the user. Siri also
uses machine learning methods to accommodate individual language usage and individual search (preferences) of the
user and offers personalized results.
AI Applications and Methodologies 137

