The most rapid route to a local installation of this model is through WSL2.
Check out the detailed setup guide below to begin.
Everything happens automatically, including the heavy cloud asset download.
To guarantee smooth performance, the process auto-selects the best options.
🔒 Hash checksum: 63e8276deb2acf0b4d407a87e111a1b5 • 📆 Last updated: 2026-06-29
Processor: high single-core performance needed for token latency
RAM: at least 32 GB in dual-channel mode for bandwidth
Disk: high-speed SSD 120 GB to cache model layers
GPU: modern architecture (Ada Lovelace / Ampere minimum)
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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