DocuSense is an intelligent document analysis platform that combines OCR, semantic search, summarization, and interactive Q&A across heterogeneous document types. It transforms static document collections into queryable knowledge bases — enabling natural language interaction with any document corpus.
The gap between storing documents and understanding them is what DocuSense closes.
Document Input (PDF, Image, Text)
│
▼
┌─────────────────┐
│ OCR Pipeline │ Tesseract + preprocessing for scanned docs
│ │ Text extraction for native PDFs
└────────┬────────┘
│
▼
┌─────────────────┐
│ Semantic Index │ Sentence Transformers embeddings
│ │ Vector storage for similarity search
└────────┬────────┘
│
┌────┴────┐
▼ ▼
┌────────┐ ┌──────────────┐
│Summarize│ │ Semantic Q&A │
│Pipeline │ │ Pipeline │
└────────┘ └──────────────┘
| Feature | Description |
|---|---|
| OCR Recognition | Extracts text from scanned documents and images |
| Semantic Search | Meaning-based search across entire document corpus |
| Auto Summarization | Abstractive and extractive document summarization |
| Interactive Q&A | Natural language queries answered from document context |
| Multi-Format | PDF, images, plain text, and mixed document types |
| Layer | Technology |
|---|---|
| Embeddings | Sentence Transformers |
| OCR | Tesseract / pytesseract |
| NLP | Transformers, spaCy |
| Backend | Python |
| Language | Python 3.10+ |
git clone https://github.com/royxlead/docusense-python.git
cd docusense-python
pip install -r requirements.txt
python app.pyDocuSense explores the practical limits of dense retrieval and semantic search on heterogeneous real-world documents — including scanned PDFs where OCR quality directly affects retrieval performance. The semantic search layer uses bi-encoder architecture for efficient similarity computation across large document collections.
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