ZacharyZhang-NY
0ee99e271d
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.
42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
from __future__ import annotations
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from functools import lru_cache
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from pathlib import Path
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from typing import Any
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class OcrResult:
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def __init__(self, text: str, confidence: float, regions: list[dict[str, Any]]) -> None:
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self.text = text
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self.confidence = confidence
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self.regions = regions
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@lru_cache(maxsize=1)
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def _rapid_ocr() -> Any:
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from rapidocr_onnxruntime import RapidOCR
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return RapidOCR()
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def read_text_from_image(path: Path) -> OcrResult:
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engine = _rapid_ocr()
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result = engine(str(path))
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raw = result[0] if isinstance(result, tuple) else result
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if raw is None:
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return OcrResult(text="", confidence=0.0, regions=[])
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texts: list[str] = []
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confidences: list[float] = []
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regions: list[dict[str, Any]] = []
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for item in raw:
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if len(item) < 3:
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continue
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box, text, confidence = item[0], str(item[1]), float(item[2])
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texts.append(text)
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confidences.append(confidence)
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regions.append({"box": box, "text": text, "confidence": confidence})
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average_confidence = sum(confidences) / len(confidences) if confidences else 0.0
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return OcrResult(text="\n".join(texts), confidence=average_confidence, regions=regions)
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