How to Setup chandra-ocr-2 Offline Setup

Docker offers the quickest path to setting up this model locally.

Refer to the instructions below to proceed.

The installer automatically pulls the model (could be multiple GBs).

The installer will automatically analyze your hardware and select the optimal configuration for your system.

🔗 SHA sum: 93d97f971985de9f276ac61ee4100bb6 | Updated: 2026-06-26 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: at least 100 GB for multiple local LLM variants Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

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.

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