Document Management System with AI-Powered Search for Scanned Documents

Document Management System with AI-Powered Search for Scanned Documents

Enhancing Document Search and Management with AI

The Ask

A client managing scanned documents needed a solution that could provide efficient search capabilities for the documented data. Challenges faced by the client:

  • Large volumes of scanned documents with limited search capabilities

  • Need to enable efficient search of both text and non-text data (e.g., images, diagrams)

  • Error-prone and time-intensive manual document processing

Our Solution

01.
AI-Powered OCR Engine
  • Built a document management system using AI-driven Optical Character Recognition (OCR) for scanned documents

01.
AI-Powered OCR Engine
  • Built a document management system using AI-driven Optical Character Recognition (OCR) for scanned documents

01.
AI-Powered OCR Engine
  • Built a document management system using AI-driven Optical Character Recognition (OCR) for scanned documents

02.
Integrated NLP and Computer Vision Models
  • Enabled search across both text and non-text data using Natural Language Processing (NLP) and computer vision models

02.
Integrated NLP and Computer Vision Models
  • Enabled search across both text and non-text data using Natural Language Processing (NLP) and computer vision models

02.
Integrated NLP and Computer Vision Models
  • Enabled search across both text and non-text data using Natural Language Processing (NLP) and computer vision models

03.
Automated Indexing and Categorization
  • Deployed automated tools for document indexing and categorization, streamlining the search process

03.
Automated Indexing and Categorization
  • Deployed automated tools for document indexing and categorization, streamlining the search process

03.
Automated Indexing and Categorization
  • Deployed automated tools for document indexing and categorization, streamlining the search process

04.
Tech Stack Employed
  • Python, Tesseract OCR, Elasticsearch, OpenCV, and NLP models

04.
Tech Stack Employed
  • Python, Tesseract OCR, Elasticsearch, OpenCV, and NLP models

04.
Tech Stack Employed
  • Python, Tesseract OCR, Elasticsearch, OpenCV, and NLP models

05.
Components Used
  • OCR engine, NLP models, Computer Vision modules, Elasticsearch for indexing and search, and custom user interfaces

05.
Components Used
  • OCR engine, NLP models, Computer Vision modules, Elasticsearch for indexing and search, and custom user interfaces

05.
Components Used
  • OCR engine, NLP models, Computer Vision modules, Elasticsearch for indexing and search, and custom user interfaces

Outcomes

01.
Enhanced Search Capabilities
  • Significantly improved search functionality, allowing for easy access to both textual and non-textual data

01.
Enhanced Search Capabilities
  • Significantly improved search functionality, allowing for easy access to both textual and non-textual data

01.
Enhanced Search Capabilities
  • Significantly improved search functionality, allowing for easy access to both textual and non-textual data

02.
Increased Efficiency
  • Automated document processing reduced manual effort and error rates, speeding up the workflow

02.
Increased Efficiency
  • Automated document processing reduced manual effort and error rates, speeding up the workflow

02.
Increased Efficiency
  • Automated document processing reduced manual effort and error rates, speeding up the workflow

03.
Improved Accessibility
  • Made critical information more accessible with powerful search and categorization features, optimizing overall document management

03.
Improved Accessibility
  • Made critical information more accessible with powerful search and categorization features, optimizing overall document management

03.
Improved Accessibility
  • Made critical information more accessible with powerful search and categorization features, optimizing overall document management

Outcomes

01.
Enhanced Search Capabilities
  • Significantly improved search functionality, allowing for easy access to both textual and non-textual data

02.
Increased Efficiency
  • Automated document processing reduced manual effort and error rates, speeding up the workflow

03.
Improved Accessibility
  • Made critical information more accessible with powerful search and categorization features, optimizing overall document management

United Kingdom

lota Analytics UK Limited

4 King's Bench Walk,

London EC4Y 7DL

United Kingdom

India

lota Analytics Private Limited 1-8 Chandigarh Technology Park, Chandigarh - 160003 India

United States

Iota Analytics Inc.

8800 Roswell Road, Bldg. C,
Suite 230, Atlanta, GA, 30350
United States

© 2024 Iota Analytics. All rights reserved.

United Kingdom

lota Analytics UK Limited

4 King's Bench Walk,

London EC4Y 7DL

United Kingdom

India

lota Analytics Private Limited 1-8 Chandigarh Technology Park, Chandigarh - 160003 India

United States

Iota Analytics Inc.

8800 Roswell Road, Bldg. C,
Suite 230, Atlanta, GA, 30350
United States

© 2024 Iota Analytics. All rights reserved.

United Kingdom

lota Analytics UK Limited

4 King's Bench Walk,

London EC4Y 7DL

United Kingdom

India

lota Analytics Private Limited 1-8 Chandigarh Technology Park, Chandigarh - 160003 India

United States

Iota Analytics Inc.

8800 Roswell Road, Bldg. C,
Suite 230, Atlanta, GA, 30350
United States

© 2024 Iota Analytics. All rights reserved.