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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.








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