- Table of Contents
- The Project
- Tools and Technologies
- Why This Project Different
- What This Project Intent For
- How it Works
- Activity Diagram
- Sequence Diagram
- Project Window
- Screenshots
- Developer Info
Working with research papers, scanned books, and technical PDFs is often frustrating.
Traditional tools like Adobe Acrobat or Foxit PDF Reader mainly rely on keyword search, which means the user must know the exact words in the document.
However, in academic or technical environments, users often search for ideas, explanations, or concepts rather than exact keywords.
This is where PDF Insights comes in.
PDF Insights is a smart PDF analysis platform designed to understand documents beyond simple text matching.
The system is capable of:
- Performing semantic search that understands meaning rather than just keywords
- Extracting text from scanned PDFs using OCR
- Detecting mathematical equations inside documents
- Converting equations into LaTeX format
- Highlighting relevant sections directly in the PDF viewer
- Exporting extracted knowledge as Markdown documents with LaTeX support
- Providing transparent processing metrics so users can understand how the system analyzed their PDF
Unlike conventional tools, PDF Insights is designed specifically for academic and technical document analysis, where context, equations, and semantic understanding are crucial.
The system runs entirely on standard CPU hardware, making it accessible for students, researchers, and institutions without requiring expensive infrastructure.
| React.js | TailwindCSS | Flask |
|---|---|---|
 |
 |
 |
| Layer | Components |
|---|---|
| Frontend | • React.js • Tailwind CSS • PDF.js |
| Backend | • Flask API • Python 3.10 |
| AI / Processing | • SentenceTransformers (all-MiniLM-L6-v2) • FAISS Vector Search • Tesseract OCR • PyMuPDF • Pix2Tex |
Most modern PDF tools provide features like:
- Basic OCR
- Keyword search
- Simple highlighting
However, these systems have several limitations.
1️⃣ Keyword-based search only
Users must type the exact words used in the document.
2️⃣ No semantic understanding
If a document says “neural networks” and the user searches “deep learning models”, traditional tools fail.
3️⃣ Poor support for mathematical documents
Many academic PDFs contain equations that cannot be extracted properly.
4️⃣ Lack of transparency
Users rarely know:
- Which pages were OCR processed
- How accurate the extraction is
- Which parts were actually analyzed
PDF Insights introduces several improvements:
✔ Semantic Search
The system understands meaning using SentenceTransformers embeddings.
✔ Vector Search Engine
Using FAISS, the system can retrieve conceptually related content quickly.
✔ Equation Detection
Mathematical expressions are detected and converted to LaTeX using Pix2Tex.
✔ Processing Transparency
The system shows:
- OCR confidence
- scanned vs text pages
- analysis metrics
✔ Research-friendly Export
Content can be exported as Markdown with LaTeX, making it easy to reuse in academic writing.
PDF Insights is designed primarily for academic and research environments.
University Students
- Reading textbooks
- Searching concepts across lecture materials
- Extracting notes from PDFs
Researchers
- Reviewing literature
- Finding relevant ideas inside long research papers
- Extracting mathematical formulas
Professors
- Preparing lecture content
- Reviewing technical documents
Data Scientists
- Exploring technical reports
- Understanding mathematical research documents
Academic Institutions
- Shared knowledge extraction
- Technical documentation analysis
The goal is to provide a tool that helps users quickly understand large academic documents without manually scanning hundreds of pages.
PDF Insights follows a multi-stage document processing pipeline.
1️⃣ The user uploads a PDF file.
2️⃣ The system detects whether pages contain text or scanned images.
3️⃣ If scanned pages exist, OCR (Tesseract) extracts the text.
4️⃣ If math detection is enabled, Pix2Tex identifies equations and converts them to LaTeX.
5️⃣ Extracted content is processed using SentenceTransformers to generate semantic embeddings.
6️⃣ The embeddings are indexed using FAISS for fast similarity search.
7️⃣ The user can then perform:
- Normal keyword search
- Semantic meaning-based search
The system highlights relevant content and OCR result can export as Markdown includes LaTeX format.
Important
For a detailed API and system architecture, Click the full documentation.
flowchart TD
A([Start]) --> B{Upload PDF}
B --> C[Detect Page Type]
C --> D{Scanned Pages}
D -->|Yes| E[Run OCR]
D -->|No| F[Extract Text]
E --> G[Compute OCR Confidence]
F --> G
G --> H{Math Detection Enabled}
H -->|Yes| I[Detect Equations]
H -->|No| J[Skip]
I --> K[Convert to LaTeX]
J --> K
K --> L[Build Semantic Index]
L --> M[Enable Search]
M --> N([Export Markdown])
sequenceDiagram
participant User
participant Frontend
participant Backend
participant OCR
participant Math
participant VectorSearch
User->>Frontend: Upload PDF
Frontend->>Backend: POST /upload
Backend->>OCR: Run OCR if needed
Backend->>Math: Detect equations
Backend->>VectorSearch: Build FAISS index
Backend-->>Frontend: Return analysis results
User->>Frontend: Semantic Query
Frontend->>Backend: POST /semantic_search
Backend->>VectorSearch: Retrieve results
VectorSearch-->>Backend: Top matches
Backend-->>Frontend: Results + page numbers
The following timeline represents the Minimum Viable Product (MVP) development schedule for PDF Insights.
gantt
title PDF Insights MVP Development Timeline
dateFormat YYYY-MM-DD
section Project Planning
Requirements Finalization :2026-03-13, 3d
System Architecture Design :2026-03-16, 4d
section Frontend Development
React Setup & Base UI Layout :2026-03-20, 5d
PDF Viewer Integration (PDF.js) :2026-03-25, 6d
Search Interface Implementation :2026-03-31, 6d
section Backend Development
Flask API Setup :2026-03-20, 5d
OCR Pipeline (Tesseract) :2026-03-26, 6d
Math Detection (Pix2Tex) :2026-04-01, 5d
Semantic Search Engine (FAISS) :2026-04-06, 6d
section Integration & Testing
Frontend–Backend Integration :2026-04-12, 5d
System Testing & Debugging :2026-04-17, 6d
Performance Optimization :2026-04-23, 4d
section Release
Documentation & Final Review :2026-04-27, 3d
MVP Release :2026-04-30, 1d
Screenshots of the application interface will be added after the UI development phase is completed.
Planned screenshots include:
- Landing page
- PDF upload interface
- Analyzer dashboard
- Semantic search results
- PDF highlight view
- Markdown export output
Irshad Hossain
Software Engineering Student
University of Frontier Technology, Bangladesh
Course
PROG 112 — Object Oriented Programming Sessional
Email irshadrisad11@gmail.com
GitHub https://github.com/Irshad-11