feat(ocr): 扫描件与图片识别——OCR 进文字层、资质库上传识别、大图浏览

计划见 docs/architecture/vision-plan.md（VS1–VS13，docs/decisions.md）。

- 验证：OCR（RapidOCR + PP-OCRv6 small，CPU）和 DeepSeek Flash 看图在国动投标文件
  和 7 张证书上初测（tech-validation §7，代码 spikes/s5-ocr/）
- OCR 服务 services/ocr（镜像、Compose 服务）：整页转正（含 180°）、红章附近按红色通道重读
- 扫描页和插图识别后并入文字层 v6，句子标 source=ocr；出处标「OCR」（迁移 0013）；
  依据识别文字的问题降级，规则不查扫描附件；国动端到端见 check-eval §16
- 资质库上传识别：添加资质对话框最上面传证书照片，识别后填表，只预填和 OCR 文字
  对得上的字段；识别值留痕、照片挂成附件（迁移 0014，提示词 vision.cert_fields v1）
- 资质库改为查看详情（只读键值 + 附件缩略图），附件用大图浏览控件看，不开新标签页
  （设计规范 §7.23）；PDF 附件按页出预览图

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

spikes/s5-ocr/synth_cer.py

@@ -0,0 +1,89 @@
+"""S5：合成扫描件上的字错误率（CER）。文字页渲染成图片 → 加扫描退化 → OCR → 和文字层比。
+
+用法：.venv/bin/python -I synth_cer.py <pdf> <页码范围> [dpi]
+退化三档：clean（只渲染）、scan（灰度、倾斜 1°、轻微模糊、噪点、JPEG 60）、bad（150 dpi 后放大、倾斜 2°、更模糊、JPEG 35）。
+文字层按页的字符顺序，比较时去掉空白；OCR 行按坐标从上到下、从左到右拼接。顺序不同会抬高 CER，所以同时报「字符包」差异（不看顺序）。
+"""
+
+from __future__ import annotations
+
+import io
+import random
+import sys
+import time
+from collections import Counter
+
+import numpy as np
+import pdfplumber
+import pypdfium2 as pdfium
+from PIL import Image, ImageFilter
+from rapidfuzz.distance import Levenshtein
+from rapidocr import RapidOCR
+
+
+
+def parse_ranges(s: str) -> set[int]:
+    out: set[int] = set()
+    for part in s.split(","):
+        a, _, b = part.partition("-")
+        out.update(range(int(a), int(b or a) + 1))
+    return out
+
+
+def degrade(img: Image.Image, level: str, rnd: random.Random) -> Image.Image:
+    if level == "clean":
+        return img
+    g = img.convert("L")
+    angle = rnd.uniform(-1, 1) * (1 if level == "scan" else 2)
+    if level == "bad":
+        g = g.resize((g.width * 3 // 4, g.height * 3 // 4)).resize((g.width, g.height))
+    g = g.rotate(angle, expand=False, fillcolor=255, resample=Image.BICUBIC)
+    g = g.filter(ImageFilter.GaussianBlur(0.6 if level == "scan" else 1.1))
+    a = np.asarray(g).astype(np.int16)
+    noise = np.random.default_rng(rnd.randint(0, 1 << 30)).integers(-25, 26, a.shape)
+    a = np.clip(a + noise, 0, 255).astype(np.uint8)
+    buf = io.BytesIO()
+    Image.fromarray(a).save(buf, "JPEG", quality=60 if level == "scan" else 35)
+    return Image.open(io.BytesIO(buf.getvalue())).convert("RGB")
+
+
+def norm(s: str) -> str:
+    return "".join(s.split()).replace("（", "(").replace("）", ")").replace("：", ":").replace("，", ",").replace("；", ";")
+
+
+def main() -> None:
+    pdf_path, pages = sys.argv[1], sorted(parse_ranges(sys.argv[2]))
+    dpi = int(sys.argv[3]) if len(sys.argv) > 3 else 200
+    eng = RapidOCR()
+    doc = pdfium.PdfDocument(pdf_path)
+    rnd = random.Random(7)
+    stats = {lv: [0, 0, 0, 0.0] for lv in ("clean", "scan", "bad")}  # 编辑距离、字符包差、总字数、秒
+    with pdfplumber.open(pdf_path) as pl:
+        for no in pages:
+            page = pl.pages[no - 1]
+            truth = norm("".join(c["text"] for c in page.chars))
+            if len(truth) < 100:
+                continue
+            base = doc[no - 1].render(scale=dpi / 72).to_pil()
+            for lv in stats:
+                img = degrade(base, lv, rnd)
+                t0 = time.time()
+                r = eng(np.asarray(img))
+                dt = time.time() - t0
+                items = sorted(zip(r.boxes, r.txts), key=lambda x: (round(x[0][0][1] / 20), x[0][0][0])) if r.boxes is not None else []
+                got = norm("".join(t for _, t in items))
+                bag = sum(((Counter(truth) - Counter(got)) + (Counter(got) - Counter(truth))).values())
+                st = stats[lv]
+                st[0] += Levenshtein.distance(truth, got)
+                st[1] += bag
+                st[2] += len(truth)
+                st[3] += dt
+            print(f"p{no} {len(truth)} chars", flush=True)
+    n = sum(1 for _ in pages)
+    for lv, (ed, bag, total, sec) in stats.items():
+        print(f"{lv:6s} CER(顺序) {ed / total:.2%}  字符包差 {bag / total / 2:.2%}  共 {total} 字  OCR {sec:.1f}s")
+    print(f"pages tried {n}, dpi {dpi}")
+
+
+if __name__ == "__main__":
+    main()