Using Docker is the absolute quickest way to install this model on your local machine.
Review and follow the instructions below.
After cloning, fire up the application using Docker.
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 | 210â¯MB |
| Supported languages | 100 |
| Input resolution | 2048â¯Ãâ¯3072â¯px |
| Processing speed | >â¯30â¯fps |
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