SOTA OCR with Core ML and dots.ocr — on-device model conversion

AI Impact Summary

SOTA OCR with Core ML and dots.ocr enables on-device optical character recognition using a 3B parameter RedNote model, surpassing Gemini 2.5 Pro in OmniDocBench. This leverages the Apple Neural Engine for high-performance, low-power processing, offering a compelling alternative to cloud-based APIs. Developers can convert models from PyTorch to Core ML, but the process requires careful attention to data types, compute units, and potential masking issues, highlighting the complexities of on-device AI deployment.