---
title: PageParse
emoji: 📄
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
---

<!--
SPDX-FileCopyrightText: 2026 Team Centurions
SPDX-License-Identifier: AGPL-3.0-or-later
-->

# PageParse

**Any handwritten page → clean structured data. On CPU. Fully offline.**

PageParse reads a scanned or photographed page of handwritten notes entirely on
your own device and turns it into structured, schema-validated records in a local
database — no GPU, no CUDA, no cloud. Point it at a handwritten page; it cleans the
image, recognises the ink on CPU, maps the messy text to a defined JSON schema, and
saves it to SQLite. It keeps working with the Wi-Fi switched off.

> Built for **The CPU-First Hackathon** — _"Build AI that runs anywhere."_

<p>
  <img alt="CPU-first"   src="https://img.shields.io/badge/inference-CPU--only-blue">
  <img alt="Offline"     src="https://img.shields.io/badge/network-offline--first-green">
  <img alt="License"     src="https://img.shields.io/badge/license-AGPL--3.0-orange">
  <img alt="Python"      src="https://img.shields.io/badge/python-3.11-yellow">
</p>

---

## Table of Contents

- [The Idea](#the-idea)
- [Why It Matters](#why-it-matters)
- [How It Works](#how-it-works)
- [Architecture](#architecture)
- [Model & Runtime (CPU-first)](#model--runtime-cpu-first)
- [Output Schema](#output-schema)
- [Data Model (SQLite)](#data-model-sqlite)
- [Tech Stack](#tech-stack)
- [Project Structure](#project-structure)
- [Getting Started](#getting-started)
- [Usage](#usage)
- [Offline-First by Design](#offline-first-by-design)
- [Quality Gates (CI/CD)](#quality-gates-cicd)
- [Judging Criteria Mapping](#judging-criteria-mapping)
- [Roadmap](#roadmap)
- [Team & Work Division](#team--work-division)
- [Contributing](#contributing)
- [License](#license)

---

## The Idea

People still write on paper — to-do lists, planners, field surveys, recipe cards,
clinic notes. That ink almost never makes it into a system you can search, sort,
or query. **PageParse bridges paper and database, entirely on-device.**

Drop in a scan or photo of a handwritten page. PageParse:

1. **reads** the handwriting on CPU,
2. **maps** it to a defined, relational schema using a small local language model, and
3. **stores** clean structured rows in SQLite you can query and export —

all while completely air-gapped.

The reference build is anchored to one note type — a **handwritten to-do / planner
page** — because the schema is tight and objectively measurable. The same engine
generalises to survey forms, recipe cards, and meeting notes with only a new schema
and grammar.

---

## Why It Matters

GPUs are scarce, costly, and online. Most computing isn't. PageParse is a
demonstration that **the CPU is enough**, and that a genuinely useful app keeps
working when the network doesn't — in clinics, fields, classrooms, and anywhere
connectivity is unreliable.

It also tackles the hackathon's core mission head-on: handwriting is about as
**unstructured** as input gets, and PageParse maps it to a strict, **structured**
schema. Handwriting recognition is a hard, real problem — and that difficulty is
exactly what makes the result worth scoring.

---

## How It Works

PageParse follows the required **Ingestion → Processing → Transformation → Storage**
workflow, with every inference step on CPU and offline.

| Stage | What happens | Runs on |
| ----- | ------------ | ------- |
| **1. Ingestion** | Load a handwritten page (JPG / PNG / PDF). | local |
| **2. Processing** | OpenCV cleanup: grayscale, deskew, adaptive threshold, denoise. | CPU |
| **3. Recognition** | On-device OCR converts ink → raw text (handwriting tier + printed fallback). | CPU |
| **4. Transformation** | A small language model maps raw text → JSON, grammar-constrained to the schema. | CPU |
| **5. Storage** | Validated rows persisted to SQLite; optional local semantic search. | local |

---

## Architecture

```
                       ┌──────────────────────────────────────────────┐
                       │                  PageParse                    │
                       │            (100% on-device, CPU)              │
                       └──────────────────────────────────────────────┘

 scan / photo       OpenCV             ONNX / Tesseract     llama.cpp + GBNF      SQLite
 JPG · PNG · PDF  ┌──────────┐       ┌────────────────┐   ┌────────────────┐   ┌──────────┐
 ───────────────▶│ Ingestion│──────▶│  Preprocessing │──▶│  OCR (on CPU)  │──▶│ Transform│──▶│ Storage │
                 │          │ clean │  deskew·binarize│   │ TrOCR / Surya /│   │ SLM→JSON │   │  + vec  │
                 └──────────┘ denoise└────────────────┘   │   Tesseract    │   │ validated│   └──────────┘
                                                          └────────────────┘   └────────────────┘
                                                                                       │
                                                                             ┌─────────▼─────────┐
                                                                             │ query · search    │
                                                                             │ export · review   │
                                                                             └───────────────────┘
```

---

## Model & Runtime (CPU-first)

Declared explicitly per the rules. **No GPU or CUDA anywhere in the inference path.**
All weights are fetched once and cached locally, so runtime needs no network.

| Stage               | Model                                   | Runtime                | Precision |
| ------------------- | --------------------------------------- | ---------------------- | --------- |
| Handwriting OCR     | TrOCR-small (handwritten) / Surya OCR 2 | ONNX Runtime (CPU EP)  | INT8      |
| Printed fallback    | Tesseract LSTM                          | Tesseract (CPU)        | —         |
| Text → schema (SLM) | Qwen2.5-1.5B-Instruct                   | llama.cpp              | Q4_K_M    |
| Semantic search     | all-MiniLM-L6-v2                        | ONNX Runtime (CPU EP)  | INT8      |

Schema validity is **guaranteed** by **llama.cpp GBNF grammar-constrained
decoding**: the model is structurally prevented from emitting anything that is not
valid against the PageParse schema, then re-validated with Pydantic.

---

## Output Schema

The structured target that unstructured handwriting is mapped to:

```json
{
  "source_page": "string (filename)",
  "captured_date": "YYYY-MM-DD",
  "tasks": [
    {
      "task": "string",
      "due_date": "YYYY-MM-DD | null",
      "priority": "high | medium | low",
      "category": "string | null",
      "status": "todo | done",
      "ocr_confidence": 0.0
    }
  ]
}
```

---

## Data Model (SQLite)

```sql
CREATE TABLE pages (
  id            INTEGER PRIMARY KEY,
  filename      TEXT,
  captured_date TEXT,
  raw_ocr_text  TEXT,
  created_at    TEXT
);

CREATE TABLE tasks (
  id             INTEGER PRIMARY KEY,
  page_id        INTEGER REFERENCES pages(id),
  task           TEXT NOT NULL,
  due_date       TEXT,
  priority       TEXT CHECK (priority IN ('high', 'medium', 'low')),
  category       TEXT,
  status         TEXT DEFAULT 'todo',
  ocr_confidence REAL
);
```

---

## Tech Stack

**Language & core**
- Python 3.11

**Computer vision / OCR**
- OpenCV — image preprocessing
- ONNX Runtime (CPU EP) — TrOCR / Surya handwriting OCR
- Tesseract — printed-text fallback

**AI / structuring**
- llama.cpp (`llama-cpp-python`) — Qwen2.5-1.5B Q4_K_M GGUF inference
- GBNF grammar — guaranteed-valid JSON
- Pydantic — schema validation

**Storage & retrieval**
- SQLite — structured persistence
- sqlite-vec + all-MiniLM-L6-v2 (ONNX) — optional semantic search

**Interface**
- Typer — CLI (primary)
- FastAPI + PWA — web interface

**Observability**
- psutil — live CPU & memory telemetry

**Tooling & quality**
- Ruff, mypy, Bandit, pip-audit, detect-secrets, pytest, gitlint, yamllint,
  markdownlint, REUSE
- pre-commit, GitLab CI (self-hosted Docker runner)

**License**
- AGPL-3.0-or-later

---

## Project Structure

```
pageparse/
├── src/
│   └── pageparse/
│       ├── __init__.py
│       ├── ingest.py            # load image / PDF input
│       ├── preprocess.py        # OpenCV cleanup pipeline
│       ├── ocr/
│       │   ├── __init__.py
│       │   ├── handwriting.py   # TrOCR / Surya (ONNX)
│       │   └── printed.py       # Tesseract fallback
│       ├── extract.py           # SLM + GBNF → JSON
│       ├── schema.py            # Pydantic models
│       ├── store.py             # SQLite persistence
│       ├── search.py            # optional semantic search
│       ├── telemetry.py         # psutil CPU/RAM panel
│       ├── config.py            # paths, model settings, airgap flag
│       ├── cli.py               # Typer CLI entry point
│       └── web.py               # FastAPI + PWA
├── grammars/
│   └── task.gbnf                # schema-constraining grammar
├── models/                      # bundled GGUF / ONNX weights (gitignored)
├── scripts/
│   ├── fetch_models.py          # one-time online model download
│   └── init_db.py               # initialise SQLite
├── samples/                     # real handwritten demo pages
├── tests/
│   ├── test_preprocess.py
│   ├── test_extract.py
│   ├── test_schema.py
│   └── test_store.py
├── web/
│   ├── static/                  # PWA assets, service worker
│   └── templates/
├── docs/
│   └── architecture.md
├── .gitlab-ci.yml
├── .pre-commit-config.yaml
├── .gitignore
├── .secrets.baseline
├── pyproject.toml
├── CONTRIBUTING.md
├── CHANGELOG.md
├── LICENSE                      # AGPL-3.0
└── README.md
```

---

## Getting Started

### Prerequisites

- Python 3.11+
- Tesseract OCR — `apt install tesseract-ocr` / `brew install tesseract`
- ~3 GB free disk for bundled models

### Installation

```bash
# Clone
git clone https://code.swecha.org/centurions/pageparse.git
cd pageparse

# Environment
python -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate

# Install (with dev tooling)
pip install -e ".[dev]"

# Fetch models ONCE while online — cached locally thereafter
python scripts/fetch_models.py

# Initialise the local database
python scripts/init_db.py
```

After `fetch_models.py` runs once, **disconnect from the network entirely** —
everything below works offline.

---

## Usage

### CLI

```bash
# Process a single handwritten page
pageparse process samples/todo_page_01.jpg

# Process a whole folder
pageparse process samples/ --batch

# Force air-gapped mode (any outbound call fails loudly)
pageparse process samples/todo_page_01.jpg --airgap

# Query and search
pageparse list --priority high
pageparse search "supplier"

# Export
pageparse export --format json > out.json
```

### Web (PWA)

```bash
pageparse serve                    # http://localhost:8000
```

Upload a page, watch the **queued → processing → saved** status, and view the
structured result. Installable as a PWA for offline use.

---

## Offline-First by Design

The core feature works with **no cloud calls**, and the demo proves it:

- **Air-gap toggle** — a UI switch and `--airgap` flag assert zero network access;
  any accidental outbound call fails loudly rather than silently.
- **Status badge** — every page shows `queued → processing → saved`, making caching
  and graceful handling observable.
- **Graceful degradation** — low-confidence OCR lines are flagged for review
  instead of crashing; pages queue and process as the CPU frees up.
- **Live resource panel** — a psutil readout of CPU % and RAM during inference, so
  the footprint is measured, not guessed.

**Demo procedure:** enable airplane mode / pull the Ethernet, then run a full
ingestion → extraction → storage cycle on a real handwritten page.

---

## Quality Gates (CI/CD)

All checks run locally via **pre-commit** and in CI on a **self-hosted GitLab
runner** (Docker executor). These are real checks — no stub jobs, no `exit 0`.

| #  | Check               | Tool             |
| -- | ------------------- | ---------------- |
| 1  | Lint                | Ruff             |
| 2  | Format              | Ruff format      |
| 3  | Type-check          | mypy             |
| 4  | Security (SAST)     | Bandit           |
| 5  | Dependency CVEs     | pip-audit        |
| 6  | Secret scan         | detect-secrets   |
| 7  | Unit tests          | pytest           |
| 8  | Semantic commits    | gitlint          |
| 9  | CI YAML lint        | yamllint         |
| 10 | Docs lint           | markdownlint     |
| 11 | License compliance  | REUSE            |
| 12 | Smoke build         | app boots headless |

```bash
pre-commit run --all-files
pytest
```

Conventional Commits are enforced (`feat:`, `fix:`, `chore:`, …).

---

## Judging Criteria Mapping

| Criterion              | How PageParse addresses it                                   |
| ---------------------- | ------------------------------------------------------------ |
| **Model performance**  | OCR confidence + extraction accuracy reported on real pages. |
| **Resource efficiency**| Live psutil CPU/RAM panel during inference.                  |
| **Offline resiliency** | Air-gap toggle; full cycle demoed with the network off.      |
| **Schema alignment**   | GBNF grammar guarantees valid JSON every run.                |
| **Graceful handling**  | Queued processing + flagged low-confidence lines + caching.  |

---

## Roadmap

- [ ] **MVP:** scanned to-do page → validated JSON → SQLite (offline, CPU)
- [ ] Printed-text fallback path
- [ ] Web PWA with air-gap toggle and status badge
- [ ] Live CPU/RAM telemetry panel
- [ ] Semantic search over extracted notes (`sqlite-vec`)
- [ ] Additional note types: survey forms, recipe cards, meeting notes
- [ ] Indic-script handwriting support

---


## Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md). Branch from `main`, keep commits
Conventional, ensure `pre-commit run --all-files` and `pytest` pass, then open a
merge request. All contributions are licensed under AGPL-3.0-or-later.

---

## License

Licensed under the **GNU Affero General Public License v3.0 or later**
(AGPL-3.0-or-later). See [LICENSE](LICENSE).

AGPL is chosen deliberately: as strong copyleft, it closes the network/SaaS
loophole that plain GPL leaves open, ensuring anyone who runs a modified PageParse
as a network service must also share their source.