Using Docker is the absolute quickest way to install this model on your local machine.
Follow the step-by-step instructions below.
Next, start the model by running the docker-compose command.
|
🧩 Hash sum → fe70005622bbbc3608b6869ad97003f5 — Update date: 2026-06-26
|
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 |