Ely-Eye — local multimodal context operating system on Qwen3.5-9B

Real-models implementation: multimodal ingestion, Context Cartridge memory,
sparse + Qwen embedding retrieval, Cache Fabric, Qwen runtime adapters, verifier
contracts, Visual HashHop proofs, BF16 LoRA fine-tuning, and a local dashboard.
Verified by a 15-check PRD proof suite on the live model.

backend/src/ely_eye/ocr.py

@@ -0,0 +1,41 @@
+from __future__ import annotations
+
+from functools import lru_cache
+from pathlib import Path
+from typing import Any
+
+
+class OcrResult:
+    def __init__(self, text: str, confidence: float, regions: list[dict[str, Any]]) -> None:
+        self.text = text
+        self.confidence = confidence
+        self.regions = regions
+
+
+@lru_cache(maxsize=1)
+def _rapid_ocr() -> Any:
+    from rapidocr_onnxruntime import RapidOCR
+
+    return RapidOCR()
+
+
+def read_text_from_image(path: Path) -> OcrResult:
+    engine = _rapid_ocr()
+    result = engine(str(path))
+    raw = result[0] if isinstance(result, tuple) else result
+    if raw is None:
+        return OcrResult(text="", confidence=0.0, regions=[])
+
+    texts: list[str] = []
+    confidences: list[float] = []
+    regions: list[dict[str, Any]] = []
+    for item in raw:
+        if len(item) < 3:
+            continue
+        box, text, confidence = item[0], str(item[1]), float(item[2])
+        texts.append(text)
+        confidences.append(confidence)
+        regions.append({"box": box, "text": text, "confidence": confidence})
+
+    average_confidence = sum(confidences) / len(confidences) if confidences else 0.0
+    return OcrResult(text="\n".join(texts), confidence=average_confidence, regions=regions)