An AI Document Processing Tool That Requires No Complex Text Extraction: No OCR

🧠 What is No OCR?

No OCR is an AI-powered document exploration tool that enables users to upload PDF files and perform search or Q&A across document collections—without extracting plain text manually. Leveraging modern embedding techniques, it supports both textual and visual queries, making it ideal for processing documents that include complex visuals and data.

🔧 Key Features

• Document Collection Management:
  Organize and manage sets of PDFs with ease.

• Automated Dataset Construction:
  Automatically converts PDFs into Hugging Face-style datasets for seamless processing.

• Vector-Based Search:
  Uses LanceDB for fast, embedding-based search across documents, including related visuals.

• Visual Question Answering (VQA):
  Integrates with open-source vision-language models like Qwen2-VL to handle advanced image-based queries.

• Docker Support:
  Fully containerized with Python backend and React frontend, enabling smooth deployment across environments.

🧪 How It Works

No OCR operates through a streamlined multi-step process:

1. Case Creation:

   • The user uploads a PDF and provides a case name.

   • The system stores the PDF locally and triggers background processing.

   • The PDF is converted into a dataset in Hugging Face format.

   • The dataset is ingested into LanceDB, where collections and datapoints are created.

   • The case is marked as complete, and a success message is displayed.

2. Search Flow:

   • The user enters a query and selects a case.

   • The system queries the LanceDB collection using text embeddings.

   • Results are retrieved and linked to the dataset.

   • Visual content is processed using VLLM (Vision-Language Large Models), and answers are returned to the user.

This process ensures a seamless, fast, and intelligent interaction with documents of all kinds.

🌐 Project Links

• Project Website: https://no-ocr.com/about

• GitHub Repository: https://github.com/kyryl-opens-ml/no-ocr

🚀 Application Scenarios

• Legal Document Analysis:
  Extract insights from legal PDFs that include tables, figures, and complex formatting.

• Academic Research:
  Explore research papers with embedded diagrams and charts, streamlining literature reviews.

• Enterprise Document Management:
  Improve internal documentation search and access efficiency across teams and departments.

• Educational Resource Discovery:
  Organize and interact with course materials and references more effectively.