Deploy chandra-ocr-2 Windows

The fastest tactical way to launch this model locally is via a Docker image.

Please adhere to the deployment steps listed below.

No manual effort needed; the setup auto-ingests the large data.

The script runs a quick hardware check to dynamically adjust parameters for elite speed.

🔗 SHA sum: 4c07e854f9d5d810bff3eb5ef5f5a9ed | Updated: 2026-07-02
  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: enough space for background apps and OS overhead
  • Disk Space: free: 80 GB on system drive for scratch space
  • Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

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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