A standalone PowerShell module provides the fastest route to local installation.

Please follow the instructions listed below to get started.

The setup auto-streams the model assets (expect a multi-GB download).

The deployment tool scans your environment and chooses the ideal parameters.

🧩 Hash sum → 1802f5f5a03b337dece47c4bf467b5ab — Update date: 2026-06-28 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: 64 GB to avoid OOM crashes on large contexts Storage: extra room for future model updates and datasets 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.

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