How to Launch chandra-ocr-2 100% Private PC Local Guide

For the fastest local setup of this model, Docker is the best choice.

Use the instructions provided below to complete the setup.

Next, execute the setup script or run docker-compose.

🔧 Digest: 987237a8a831d168f8ac9f3d016f2429 • 🕒 Updated: 2026-06-21 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space:70 GB free space for full FP16 weights storage Graphics: 12 GB VRAM minimum required for basic quantization

The **chandra-ocr-2** model delivers *state-of-the-art* optical character recognition with unprecedented accuracy across diverse document types. It leverages a deep convolutional neural network architecture combined with attention mechanisms to capture both fine-grained character shapes and contextual layout cues. The model supports a wide range of languages and scripts, making it suitable for global enterprise workflows. Performance benchmarks show a character error rate below 0.5% on standard benchmarks, outperforming previous generations by over 15%. Integration is streamlined via a lightweight API that processes images in *real-time* with minimal hardware requirements.

Specification	Value
Model size	210 MB
Supported languages	100
Input resolution	2048 × 3072 px
Processing speed	> 30 fps

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