How to Deploy chandra-ocr-2 Locally via Ollama 2 One-Click Setup Step-by-Step
Monday, June 29th, 2026, 2:05 pm
Running this model locally is fastest when deployed through Docker.
Please follow the instructions listed below to get started.
The system automatically triggers a cloud download for all heavy weights.
The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.
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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