# Chandra 2.0 OCR — Assessment for HoffDesk

**Date:** 2026-04-21  
**Source:** datalab-to/chandra (GitHub), Apache 2.0 + OpenRAIL-M  
**Researcher:** Wadsworth 📋

---

## Executive Summary

**Verdict: High potential, deferred implementation.**

Chandra 2.0 is a state-of-the-art open-source OCR model that significantly outperforms alternatives on complex documents. However, it requires substantial GPU resources (H100-class for production throughput) and has unclear immediate value for HoffDesk's current workflows.

---

## What It Does Exceptionally Well

| Capability | Performance | Notes |
|------------|-------------|-------|
| **Complex tables** | 89.9% (olmocr benchmark) | Best-in-class for financial/scientific tables |
| **Math/STEM** | 89.3% | LaTeX-aware extraction |
| **Handwriting** | Strong | Cursive, mixed print/cursive |
| **Multilingual** | 77.8% avg (90 languages) | Beats Gemini 2.5 Flash |
| **Layout preservation** | Native | Outputs Markdown + HTML + JSON with structure |
| **Forms/checkboxes** | Reconstructs accurately | Critical for scanned documents |
| **Old scans** | 49.8% (best available) | Handles degraded historical docs |

**Key differentiator:** Unlike simple OCR, Chandra preserves *document structure* — columns, tables, headers, captions, and reading order.

---

## Technical Requirements

| Mode | Requirements | Throughput | Best For |
|------|--------------|------------|----------|
| **vLLM Server** | H100 80GB (or equivalent) | 1.44 pages/sec | Production batch processing |
| **HuggingFace** | 24GB+ VRAM (RTX 3090/4090) | Slower | Local development, small batches |
| **Hosted API** | API key ($) | Fastest | Evaluation, low-volume needs |

---

## HoffDesk Fit Analysis

### Current Workflows — Low Match

| Workflow | OCR Need? | Chandra Value |
|----------|-----------|---------------|
| **Family calendar** | No | None — structured data from APIs |
| **Blog content** | No | None — Markdown authoring |
| **Email processing** | Minimal | Could extract PDF attachments, but rare |
| **Agent coordination** | No | Text-based already |

### Potential Future Workflows — High Match

| Use Case | Value | Priority |
|----------|-------|----------|
| **Document archive** | Convert scanned family docs → searchable Markdown | Medium |
| **Receipt/expense extraction** | Table extraction from photos | Medium |
| **Historical photo OCR** | Extract text from old scanned photos | Low |
| **Kids' homework help** | Math problem extraction from photos | Low |
| **Mail digitization** | Scan postal mail → structured data | Low |

---

## Integration Path (If Pursued)

### Option 1: Hosted API (Immediate)
- **Effort:** Minimal — REST API calls
- **Cost:** ~$0.01/page at volume
- **Pros:** No infrastructure, best accuracy
- **Cons:** External dependency, ongoing cost, data leaves home

### Option 2: Local vLLM (Beelink insufficient)
- **Effort:** High — requires GPU server setup
- **Hardware:** Need dedicated inference box (H100 or 2x A6000)
- **Cost:** $3K–$15K hardware or cloud GPU
- **Pros:** Sovereign, no external data
- **Cons:** Capital expense, power, maintenance

### Option 3: Gaming PC (RTX 3080 Ti) — Marginal
- **Effort:** Medium — HuggingFace backend
- **Throughput:** ~0.3 pages/sec (estimated)
- **Pros:** Uses existing hardware
- **Cons:** Slow for batch jobs, blocks gaming use

---

## Competitive Landscape

| Model | Accuracy | Speed | Cost | Self-Hosted |
|-------|----------|-------|------|-------------|
| **Chandra 2** | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | $$$ | ✅ |
| **olmOCR 2** | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | Free | ✅ |
| **Mistral OCR** | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | $ | ❌ |
| **GPT-4o Vision** | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | $$ | ❌ |
| **Tesseract** | ⭐⭐ | ⭐⭐⭐⭐⭐ | Free | ✅ |

Chandra wins on accuracy for complex documents. olmOCR 2 is the open-source alternative with better speed/accuracy tradeoff for simpler docs.

---

## Recommendation

**Defer implementation.** 

Chandra 2.0 is impressive technology without a clear immediate use case for HoffDesk. Current workflows don't involve document scanning or complex OCR needs.

**Revisit when:**
- Document archive workflow becomes priority
- Receipt/expense tracking enters roadmap
- Kids start bringing home complex math worksheets requiring digitization

**If revisiting:**
1. Start with hosted API for evaluation
2. Measure actual usage patterns
3. Justify local GPU investment only if volume warrants

---

## Quick Test (Optional)

If curious, can install on Gaming PC for single-page tests:

```bash
pip install chandra-ocr[hf]
chandra ~/test-receipt.jpg ./output --method hf
```

This validates quality without infrastructure commitment.

---

*Assessment complete. Technology filed for future reference.*