Intelligent &
Document Parser

Experience our Intelligent Document Parsing, a solution that breaks down documents into manageable parts to swiftly extract key data. Leveraging advanced algorithms, it recognizes patterns and keywords, transforming unstructured data into structured formats. Perfect for enhancing data analysis and content management, it's a game-changer for information processing.

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Document Parser

Experience our Intelligent Document Parsing, a solution that breaks down documents into manageable parts to swiftly extract key data. Leveraging advanced algorithms, it recognizes patterns and keywords, transforming unstructured data into structured formats. Perfect for enhancing data analysis and content management, it's a game-changer for information processing.

Get Started

Product Use Cases

Text/Intent Classification

Problem Statement

Many NLP applications such as sentiment analysis, topic classification, spam detection, customer feedback analysis, Chabot, virtual assistant, etc. requires large amount datasets in which text and intents are classified. Text classification mainly deals with assigning predefined labels or categories to text datasets while intent classification focuses on determining the intention or purpose behind a user’s text or query. Doing this text/intent classification on large amount of dataset becomes cumbersome and NS document parser helps to simplify this task

Solution

  • NS document parser allows user to customize UI to set up categories or labels as per their requirement.
  • Easy integration with NS intelligent workflow allows to collaborate and review allocates tags to datasets.

Unstructured Data Extraction

Problem Statement

Many application requires to extract information from documents which is in unstructured form. Post extraction, based on layout of the document, information needs to be converted into structured form. This structured information is then utilized for automated data entry, insights generation, etc. Main challenge is to extract the structured information accurately when the layout of source documents is varying. NS document parser simplifies this process and allows to handle extraction at larger scale in a robust way.

Solution

  • NS document parser can read documents from various sources, such as file uploads, email attachments, or document repositories.
  • Ability to extract text from images using OCR, layout analysis to identify sections and elements, and semantic analysis to interpret the meaning of the text.
  • Rule engine allows to build validation rule with cross-referencing with external sources, or human-in-the-loop verification processes.
  • Extraction output can be exported in JSON, XML, CSV or integrated with database for downstream application uses.
  • Easy integration with NS Data Annotation tool to allow to capture additional metadata.

Product Use Cases

Text/Intent Classification

Problem Statement

Many NLP applications such as sentiment analysis, topic classification, spam detection, customer feedback analysis, Chabot, virtual assistant, etc. requires large amount datasets in which text and intents are classified. Text classification mainly deals with assigning predefined labels or categories to text datasets while intent classification focuses on determining the intention or purpose behind a user’s text or query. Doing this text/intent classification on large amount of dataset becomes cumbersome and NS document parser helps to simplify this task

Solution

  • NS document parser allows user to customize UI to set up categories or labels as per their requirement.
  • Easy integration with NS intelligent workflow allows to collaborate and review allocates tags to datasets.

Unstructured Data Extraction

Problem Statement

Many application requires to extract information from documents which is in unstructured form. Post extraction, based on layout of the document, information needs to be converted into structured form. This structured information is then utilized for automated data entry, insights generation, etc. Main challenge is to extract the structured information accurately when the layout of source documents is varying. NS document parser simplifies this process and allows to handle extraction at larger scale in a robust way.

Solution

  • NS document parser can read documents from various sources, such as file uploads, email attachments, or document repositories.
  • Ability to extract text from images using OCR, layout analysis to identify sections and elements, and semantic analysis to interpret the meaning of the text.
  • Rule engine allows to build validation rule with cross-referencing with external sources, or human-in-the-loop verification processes.
  • Extraction output can be exported in JSON, XML, CSV or integrated with database for downstream application uses.
  • Easy integration with NS Data Annotation tool to allow to capture additional metadata.

Product Use Case

Product Use Case