govdoc-md-cleaner v0.1.0: 政务文档 Markdown 清洗工具(规则引擎 + CLI + 测试)

- 8 条 YAML 声明规则:页码/页眉页脚/目录点线/图片/HTML表格/散落标签/行尾空白/空行
- 防误伤设计:protect 正则 + 内容形态豁免 + OCR burst 检测
- md-clean single/batch CLI,JSON 清洗报告
- 18 个单元测试

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
2026-08-20 17:04:10 +08:00
commit c19e9fcebf
10 changed files with 814 additions and 0 deletions
+9
View File
@@ -0,0 +1,9 @@
# 数据与产物
*.cleaned.md
report.json
__pycache__/
*.pyc
.venv/
dist/
*.egg-info/
+93
View File
@@ -0,0 +1,93 @@
# govdoc-md-cleaner
政务文档(招标 / 投标 / 采购 / 合同)**PDF→Markdown 产物**的规则化清洗工具。
上游解析管线(MinerU / OCR)把 PDF 转成 Markdown 后,会带入大量非正文噪音:
逐页重复的页眉页脚、页码行、目录点线、死链图片引用、压成单行的 HTML 表格、
行尾硬换行双空格,以及 OCR 重复崩坏段。本工具用一套 **YAML 声明、按序应用、
逐条统计** 的规则把它们清掉,输出可直接进入比对 / RAG / 审核管线的干净 Markdown。
## 数据来源
清洗目标为 `/home/lihaoze/gov_test_data`(律所上传的真实政务文件的筛选子集,
详见其 `compare/readme.md`),未来会持续接入更多批次的 Markdown 文件。
**本项目只包含清洗代码与规则,不包含任何业务数据**——测试数据不入库。
## 清洗规则(rules/default.yaml
| 顺序 | 规则 | 处理对象 | 示例 |
|---|---|---|---|
| 10 | `strip_page_lines` | 页码行 | `第 3 页 共 53 页``第4页共4页``Page 3 of 10``- 4 -` |
| 20 | `strip_repeated_short_lines` | 页眉页脚 + OCR 崩坏 | 全篇重复 ≥3 次的短行;连续刷屏 ≥5 次(实测"审计程序"×1359 |
| 30 | `strip_toc_dots` | 目录点线 | `第一章 投标邀请函 ………… 2` → 保留标题,删点线页码 |
| 40 | `drop_images` | 死链图片 | `![](images/xxx.jpg)`、base64 内嵌图 |
| 50 | `normalize_tables` | HTML 表格 | `<table><tr><td>…` 单行压缩 → Markdown 管道表格 |
| 60 | `strip_stray_html` | 散落标签 | `<br/>` |
| 70 | `rstrip_lines` | 行尾空白 | MinerU 每行行尾的双空格硬换行 |
| 80 | `collapse_blank_lines` | 空行折叠 | 连续空行 → 1 行;裁掉文首文末 |
### 防误伤设计(来自真实数据踩坑)
- **protect 正则**`投标人:(公章)``法定代表人签名:``日期: 年 月 日`
`致:xxx`、声明函结尾句——标书里逐章重复,**是正文模板不是页眉**,默认保护。
- **内容形态豁免**:编号条款 `1)…``1、…``一、…`、列表/表格行、
`乙方:xxx` 字段行——平行结构天然重复,引擎侧直接不参与页眉判定。
- **burst 检测**:同一短行连续刷屏 ≥5 次(间隔 ≤2 行)判为 OCR 重复崩坏,
直接删除(003-10 案例出现"审计程序"连续 1359 行)。
## 安装
```bash
pip install -e . # 或直接 python -m cleaner.cli(仅需 PyYAML
```
## 使用
```bash
# 单文件:清洗 + 打印统计
md-clean single input.md -o output.md --diff
# 批量:递归清洗目录下所有 .md,输出到平行目录 + JSON 报告
md-clean batch /path/to/uploads -o /path/to/uploads_cleaned \
--pattern "*.md" --report report.json
# 用自定义规则集(复制 default.yaml 改参数即可)
md-clean batch uploads/ --rules rules/strict.yaml
```
`--diff` 输出 unified diff`report.json` 记录每个文件每条规则的命中数,
清洗过程完全可审计、可回滚(重跑即得原结果的对照)。
## 项目结构
```
govdoc-md-cleaner/
├── cleaner/
│ ├── cleaner.py # 引擎:按序应用规则,输出 CleanResult(text, stats)
│ ├── rules.py # 规则实现 + REGISTRY + YAML 加载
│ └── cli.py # single / batch 两个子命令
├── rules/
│ └── default.yaml # 默认规则集(顺序、开关、参数、protect 列表)
├── tests/
│ └── test_rules.py # 每条规则的最小样例 + 防误伤回归
└── docs/
└── RULES.md # 规则编写指南(新增规则的方法)
```
## 新增一条规则
1. `cleaner/rules.py`:实现 `_your_rule(text, params, stats) -> text`,注册进 `REGISTRY`
2. `rules/default.yaml`:追加 `- name: your_rule / order: / params:`
3. `tests/test_rules.py`:加一个真实数据浓缩出的最小样例。
原则:**规则只删噪音不删正文**;拿不准的形态默认保留,靠 protect/exempt 收紧。
## 测试
```bash
python -m unittest discover tests -v
```
## License
MIT
+11
View File
@@ -0,0 +1,11 @@
"""govdoc-md-cleaner: 政务文档 Markdown 清洗工具包。
针对 PDF→Markdown 转换产物(MinerU / OCR 管线输出)的常见脏数据,
提供基于 YAML 规则的、可复现、可审计的清洗能力。
"""
from cleaner.cleaner import MarkdownCleaner, CleanResult, load_rules
from cleaner.cli import main
__version__ = "0.1.0"
__all__ = ["MarkdownCleaner", "CleanResult", "load_rules", "main"]
+56
View File
@@ -0,0 +1,56 @@
"""清洗引擎:按规则顺序应用,输出清洗后文本 + 统计报告。"""
from __future__ import annotations
import difflib
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional
from cleaner.rules import REGISTRY, Rule, load_rules
@dataclass
class CleanResult:
"""一次清洗的结果:产物 + 可审计的统计。"""
text: str
stats: Dict[str, int] = field(default_factory=dict)
rules_applied: List[str] = field(default_factory=list)
@property
def total_hits(self) -> int:
return sum(v for k, v in self.stats.items() if k != "collapsed_blanks")
class MarkdownCleaner:
def __init__(self, rules: Optional[List[Rule]] = None, rules_path: Optional[Path] = None):
self.rules = rules if rules is not None else load_rules(rules_path)
def clean_text(self, text: str) -> CleanResult:
stats: Dict[str, int] = {}
applied: List[str] = []
for rule in self.rules:
if not rule.enabled:
continue
func = REGISTRY[rule.name]
text = func(text, rule.params, stats)
applied.append(rule.name)
return CleanResult(text=text, stats=stats, rules_applied=applied)
def clean_file(self, src: Path, dst: Optional[Path] = None) -> CleanResult:
raw = Path(src).read_text(encoding="utf-8")
result = self.clean_text(raw)
if dst is not None:
Path(dst).parent.mkdir(parents=True, exist_ok=True)
Path(dst).write_text(result.text, encoding="utf-8")
return result
@staticmethod
def diff(before: str, after: str, context: int = 1) -> str:
return "\n".join(
difflib.unified_diff(
before.splitlines(), after.splitlines(),
fromfile="before", tofile="after", lineterm="", n=context,
)
)
+89
View File
@@ -0,0 +1,89 @@
"""命令行入口。
用法:
md-clean single <input.md> [-o output.md] [--diff] [--rules rules.yaml]
md-clean batch <dir> [-o outdir] [--pattern "*.md"] [--report report.json]
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
from typing import List, Optional
from cleaner.cleaner import MarkdownCleaner
from cleaner.rules import load_rules
def _build(path: Optional[Path]) -> MarkdownCleaner:
return MarkdownCleaner(rules=load_rules(path))
def cmd_single(args: argparse.Namespace) -> int:
cleaner = _build(args.rules)
src = Path(args.input)
raw = src.read_text(encoding="utf-8")
result = cleaner.clean_text(raw)
if args.output:
Path(args.output).parent.mkdir(parents=True, exist_ok=True)
Path(args.output).write_text(result.text, encoding="utf-8")
print(f"已写入 {args.output}")
if args.diff:
print(MarkdownCleaner.diff(raw, result.text))
print(json.dumps(result.stats, ensure_ascii=False, indent=2))
return 0
def cmd_batch(args: argparse.Namespace) -> int:
cleaner = _build(args.rules)
src_dir = Path(args.directory)
files = sorted(p for p in src_dir.rglob(args.pattern) if p.is_file())
if not files:
print(f"{src_dir} 下未找到匹配 {args.pattern} 的文件", file=sys.stderr)
return 1
out_dir = Path(args.output) if args.output else src_dir.parent / (src_dir.name + "_cleaned")
report = []
for f in files:
rel = f.relative_to(src_dir)
dst = out_dir / rel
result = cleaner.clean_file(f, dst)
report.append({"file": str(rel), "stats": result.stats})
hits = result.total_hits
print(f"[ok] {rel} 命中 {hits}")
if args.report:
Path(args.report).parent.mkdir(parents=True, exist_ok=True)
Path(args.report).write_text(
json.dumps({"files": report}, ensure_ascii=False, indent=2), encoding="utf-8"
)
print(f"报告已写入 {args.report}")
print(f"共清洗 {len(files)} 个文件 → {out_dir}")
return 0
def main(argv: Optional[List[str]] = None) -> int:
parser = argparse.ArgumentParser(prog="md-clean", description="政务文档 Markdown 清洗工具")
sub = parser.add_subparsers(dest="command", required=True)
p1 = sub.add_parser("single", help="清洗单个文件")
p1.add_argument("input", help="输入 .md 文件")
p1.add_argument("-o", "--output", help="输出路径(缺省打印统计不写文件)")
p1.add_argument("--diff", action="store_true", help="打印 unified diff")
p1.add_argument("--rules", type=Path, help="规则 YAML 路径")
p2 = sub.add_parser("batch", help="批量清洗目录(递归)")
p2.add_argument("directory", help="输入目录")
p2.add_argument("-o", "--output", help="输出目录(缺省 <dir>_cleaned")
p2.add_argument("--pattern", default="*.md", help="文件 glob(默认 *.md")
p2.add_argument("--report", help="清洗报告 JSON 输出路径")
p2.add_argument("--rules", type=Path, help="规则 YAML 路径")
args = parser.parse_args(argv)
if args.command == "single":
return cmd_single(args)
return cmd_batch(args)
if __name__ == "__main__":
sys.exit(main())
+281
View File
@@ -0,0 +1,281 @@
"""规则模型:一条清洗规则 = 名称 + 开关 + 参数 + 应用顺序。
规则用 YAML 声明(rules/*.yaml),引擎按 order 依次应用,
这样清洗过程可复现、可 diff、可回滚。
"""
from __future__ import annotations
import re
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional
import yaml
@dataclass
class Rule:
"""一条清洗规则。
name: 唯一标识(报告里引用)
order: 应用顺序,小的先执行
enabled: 开关,方便对某个用例单独关掉
params: 传给处理函数的额外参数
"""
name: str
order: int = 100
enabled: bool = True
params: Dict[str, Any] = field(default_factory=dict)
@staticmethod
def from_dict(d: Dict[str, Any]) -> "Rule":
return Rule(
name=d["name"],
order=int(d.get("order", 100)),
enabled=bool(d.get("enabled", True)),
params=dict(d.get("params") or {}),
)
# ---------------------------------------------------------------------------
# 每条规则的具体实现。函数签名统一为 (text, params, stats) -> text。
# stats 是 {rule_name: 删改行数},用于生成清洗报告。
# ---------------------------------------------------------------------------
RuleFunc = Callable[[str, Dict[str, Any], Dict[str, int]], str]
# 页码类:第X页 共Y页 / 第X页共Y页 / Page x of y / - 3 - 等
_RE_PAGE_CN = re.compile(
r"^[ \t]*第\s*[0-9-]+\s*页\s*(?:[,/]?\s*共\s*[0-9-]+\s*页)?[ \t]*$"
)
_RE_PAGE_EN = re.compile(
r"^[ \t]*(?:[-–—]?\s*Page\s+\d+(?:\s+of\s+\d+)?\s*[-–—]?"
r"|[-–—]\s*\d{1,4}\s*[-–—]"
r"|\d+\s*/\s*\d+)[ \t]*$",
re.IGNORECASE,
)
# 纯页码数字行(单独一行只有 1-4 位数字,且不是标题编号场景)
_RE_PAGE_BARE = re.compile(r"^[ \t]*\d{1,4}[ \t]*$")
def _strip_page_lines(text: str, params: Dict[str, Any], stats: Dict[str, int]) -> str:
keep_bare_numbers = bool(params.get("keep_bare_numbers", True))
out, n = [], 0
for line in text.splitlines():
if _RE_PAGE_CN.match(line) or _RE_PAGE_EN.match(line):
n += 1
continue
if not keep_bare_numbers and _RE_PAGE_BARE.match(line):
n += 1
continue
out.append(line)
stats["page_lines"] = stats.get("page_lines", 0) + n
return "\n".join(out)
# 页眉/页脚:同一短行在全篇重复出现 >= N 次(默认 3),视为页眉页脚删除。
# 两道保险避免误伤正文:
# 1. protect 正则 —— 标书里逐章重复的模板行(签章/日期/声明结尾)
# 2. 内容形态行直接豁免 —— 编号条款 "(1)…"/"1、…"/"一、…"/列表/表格行
# 在平行结构的标书里天然重复,但它们是正文不是页眉。
_RE_CONTENT_LIKE = re.compile(
r"^[\d-(\[【\-–—*•·①-⑳一二三四五六七八九十百第章节条款、,.。::|]"
r"|[一-鿿]{1,6}[:]\s*\S" # "乙方:xxx" / "地址:xxx" 这类字段行
r"|^[一-鿿A-Za-z]{1,6}[:]\s*$" # "乙方:" / "注:" 字段标签行
)
def _strip_repeated_short_lines(
text: str, params: Dict[str, Any], stats: Dict[str, int]
) -> str:
threshold = int(params.get("threshold", 3))
max_len = int(params.get("max_len", 40))
# 同一短行在文中连续出现 >= burst_limit 次(间隔 <= burst_gap 行)视为
# OCR 重复崩坏(如 003-10 案例"审计程序"连续刷屏 1359 次),无论阈值直接删。
burst_limit = int(params.get("burst_limit", 5))
burst_gap = int(params.get("burst_gap", 2))
protect = [re.compile(p) for p in params.get("protect", [])]
lines = text.splitlines()
counts: Dict[str, int] = {}
positions: Dict[str, List[int]] = {}
for i, line in enumerate(lines):
s = line.strip()
if (
0 < len(s) <= max_len
and not s.startswith("#")
and not _RE_CONTENT_LIKE.match(s)
and "![" not in s
):
counts[s] = counts.get(s, 0) + 1
positions.setdefault(s, []).append(i)
repeated = {
s
for s, c in counts.items()
if c >= threshold and not any(rx.search(s) for rx in protect)
}
# OCR 崩坏连续段:即使该行被 protect/内容豁免,连续刷屏也删
for s, pos in positions.items():
best_run = run = 1
for a, b in zip(pos, pos[1:]):
run = run + 1 if b - a <= burst_gap else 1
best_run = max(best_run, run)
if best_run >= burst_limit:
repeated.add(s)
if not repeated:
return text
out, n = [], 0
for line in lines:
if line.strip() in repeated:
n += 1
continue
out.append(line)
stats["header_footer_lines"] = stats.get("header_footer_lines", 0) + n
return "\n".join(out)
# 目录点线:标题文字 ………… 12 / ······ 3 之类(… U+2026 也算;# 前缀可选)
_RE_TOC_DOTS = re.compile(
r"^(#{1,6}\s+)?.*?[ \t]*[\.。·•‧…]{6,}[ \t]*[\d-]*[ \t]*$"
)
def _strip_toc_dots(text: str, params: Dict[str, Any], stats: Dict[str, int]) -> str:
out, n = [], 0
for line in text.splitlines():
m = _RE_TOC_DOTS.match(line)
if m:
# 去掉点线和页码,保留标题文字;纯点线+页码的目录行整行删
title = re.sub(r"[ \t]*[\.。·•‧…]{6,}[ \t]*[\d-]*[ \t]*$", "", line).rstrip()
if title.strip() and not re.fullmatch(r"[\.。·•‧…\d-\s]+", title):
out.append(title)
n += 1
continue
out.append(line)
stats["toc_dot_lines"] = stats.get("toc_dot_lines", 0) + n
return "\n".join(out)
# 图片引用:![](images/xxx.jpg) —— 图片目录不在交付物里,引用是死链
_RE_IMAGE = re.compile(r"[ \t]*!\[[^\]]*\]\([^)]*\)[ \t]*")
def _drop_images(text: str, params: Dict[str, Any], stats: Dict[str, int]) -> str:
placeholder = params.get("placeholder") # None=整行删除;否则替换为占位文本
out, n = [], 0
for line in text.splitlines():
if _RE_IMAGE.fullmatch(line):
n += 1
if placeholder:
out.append(str(placeholder))
continue
new = _RE_IMAGE.sub("", line)
if new != line:
n += 1
line = new.rstrip()
out.append(line)
stats["image_refs"] = stats.get("image_refs", 0) + n
return "\n".join(out)
# HTML 表格规范化:<table ...><tr><td>…</td></tr></table> 压缩为合法 Markdown 管道表格
_RE_TABLE = re.compile(r"<table[^>]*>(.*?)</table>", re.DOTALL | re.IGNORECASE)
_RE_TR = re.compile(r"<tr[^>]*>(.*?)</tr>", re.DOTALL | re.IGNORECASE)
_RE_TD = re.compile(r"<t[dh][^>]*>(.*?)</t[dh]>", re.DOTALL | re.IGNORECASE)
def _cell_text(raw: str) -> str:
cell = re.sub(r"<br\s*/?>", " ", raw, flags=re.IGNORECASE)
cell = re.sub(r"<[^>]+>", "", cell)
return " ".join(cell.split()).replace("|", "\\|")
def _table_to_md(tbl_html: str) -> str:
rows: List[List[str]] = []
for tr in _RE_TR.findall(tbl_html):
cells = [_cell_text(td) for td in _RE_TD.findall(tr)]
if cells:
rows.append(cells)
if not rows:
return ""
width = max(len(r) for r in rows)
rows = [r + [""] * (width - len(r)) for r in rows]
lines = ["| " + " | ".join(rows[0]) + " |", "|" + "---|" * width]
lines.extend("| " + " | ".join(r) + " |" for r in rows[1:])
return "\n".join(lines)
def _normalize_tables(text: str, params: Dict[str, Any], stats: Dict[str, int]) -> str:
def _sub(m: re.Match[str]) -> str:
md = _table_to_md(m.group(1))
return md if md else ""
new, n = _RE_TABLE.subn(_sub, text)
# 残缺兜底:文档截断导致 <table> 未闭合时,把剩余 <tr> 行也转掉
if "<table" in new:
tail_i = new.rfind("<table")
head, tail = new[:tail_i], new[tail_i:]
tail = _RE_TR.sub(
lambda m: "\n" + _table_to_md("<table>" + m.group(0) + "</table>"),
tail,
)
tail = re.sub(r"</?table[^>]*>", "", tail)
new = head + tail
n += 1
stats["html_tables"] = stats.get("html_tables", 0) + n
return new
# 表格内 <br/> 会被上面规则拍平;这里处理散落的 HTML 换行/空白标签
def _strip_stray_html(text: str, params: Dict[str, Any], stats: Dict[str, int]) -> str:
new = re.sub(r"<br\s*/?>", " ", text, flags=re.IGNORECASE)
if new != text:
stats["stray_html"] = stats.get("stray_html", 0) + text.count("<br")
return new
# 行尾双空格(MinerU 每行都带,语义是硬换行,清洗后统一去掉)
def _rstrip_lines(text: str, params: Dict[str, Any], stats: Dict[str, int]) -> str:
out, n = [], 0
for line in text.splitlines():
stripped = line.rstrip()
if stripped != line:
n += 1
out.append(stripped)
stats["trailing_ws_lines"] = stats.get("trailing_ws_lines", 0) + n
return "\n".join(out)
# 连续空行压成一行;文首文末空白裁掉
def _collapse_blank_lines(
text: str, params: Dict[str, Any], stats: Dict[str, int]
) -> str:
text = re.sub(r"[ \t]*\n(?:[ \t]*\n){2,}", "\n\n", text)
text = text.strip("\n") + "\n" if text.strip() else ""
stats["collapsed_blanks"] = stats.get("collapsed_blanks", 1)
return text
REGISTRY: Dict[str, RuleFunc] = {
"strip_page_lines": _strip_page_lines,
"strip_repeated_short_lines": _strip_repeated_short_lines,
"strip_toc_dots": _strip_toc_dots,
"drop_images": _drop_images,
"normalize_tables": _normalize_tables,
"strip_stray_html": _strip_stray_html,
"rstrip_lines": _rstrip_lines,
"collapse_blank_lines": _collapse_blank_lines,
}
def load_rules(path: Optional[Path] = None) -> List[Rule]:
"""从 YAML 加载规则;未指定路径时用包内默认规则。"""
if path is None:
path = Path(__file__).resolve().parent.parent / "rules" / "default.yaml"
data = yaml.safe_load(Path(path).read_text(encoding="utf-8")) or {}
rules = [Rule.from_dict(d) for d in data.get("rules", [])]
unknown = [r.name for r in rules if r.name not in REGISTRY]
if unknown:
raise ValueError(f"未知规则: {unknown},可用规则: {sorted(REGISTRY)}")
return sorted(rules, key=lambda r: r.order)
+45
View File
@@ -0,0 +1,45 @@
# 规则编写指南
一条清洗规则 = `cleaner/rules.py` 里的一个函数 + `rules/*.yaml` 里的一条声明。
## 函数签名
```python
def _your_rule(text: str, params: dict, stats: dict) -> str:
...
stats["your_hits"] = stats.get("your_hits", 0) + n # 计入报告
return new_text
```
- 输入输出都是**整篇文本**;引擎按 `order` 从小到大依次调用。
- `params` 来自 YAML,改参数不用改代码。
- `stats` 的 key 会出现在 `report.json`,命名用蛇形复数(如 `page_lines`)。
## YAML 声明
```yaml
rules:
- name: your_rule # 必须与 REGISTRY 键一致,加载时校验
order: 55 # 应用顺序;同段处理尽量插在相关规则之间
enabled: true # 某用例不适用的规则可单关
params:
threshold: 3
protect: ["正则1", "正则2"]
```
## 设计守则(从 gov_test_data 踩坑总结)
1. **只删噪音,不删正文**。拿不准的形态默认保留,宁可漏删不可误删。
2. **重复 ≠ 页眉**。标书是平行模板文档:签章栏、日期栏、声明结尾句、
编号条款都会重复出现。判定页眉前先过:
- `protect` 正则(业务模板白名单)
- 内容形态豁免(编号/列表/表格/字段行)
- burst 检测(连续刷屏才是 OCR 崩坏)
3. **每个规则独立可测**。tests/ 里用真实数据浓缩的最小样例做回归,
尤其是防误伤样例(protect 命中、编号条款保留)。
4. **统计必须可见**。每条规则报告命中数,批量清洗后扫一眼 report.json
就能发现某条规则突然命中异常(多半是误伤)。
## 已知规则明细
`rules/default.yaml` 内注释与 README 规则表。
+21
View File
@@ -0,0 +1,21 @@
[build-system]
requires = ["setuptools>=68"]
build-backend = "setuptools.build_meta"
[project]
name = "govdoc-md-cleaner"
version = "0.1.0"
description = "政务文档(招标/投标/采购/合同)PDF→Markdown 产物的清洗工具"
readme = "README.md"
requires-python = ">=3.10"
license = { text = "MIT" }
dependencies = ["PyYAML>=6.0"]
[project.scripts]
md-clean = "cleaner.cli:main"
[tool.setuptools.packages.find]
include = ["cleaner*"]
[tool.setuptools.package-data]
cleaner = ["../rules/*.yaml"]
+55
View File
@@ -0,0 +1,55 @@
# 政务文档 Markdown 默认清洗规则集
# 每条规则: name(必填,须在引擎 REGISTRY 中注册) / order(应用顺序) / enabled / params
# 针对 PDF→Markdown 管线(MinerU/OCR)产物的典型脏数据。
rules:
# 1) 页码行:"第 X 页 共 Y 页" / "第X页共4页" / "Page 3 of 10" / "3 / 10"
- name: strip_page_lines
order: 10
params:
keep_bare_numbers: true # 单独一行纯数字(可能是页码也可能是编号),默认保留
# 2) 页眉页脚 + OCR 重复崩坏行:
# a) 同一短行全篇重复 >= threshold 次(如逐页出现的项目名、"正本")
# b) 同一短行连续刷屏 >= burst_limit 次(OCR 崩坏,如"审计程序"×1359
# protect 列出"重复但属于正文模板"的保护正则(标书里逐章出现的签章/日期栏)
# 内容形态行(编号条款/列表/表格行)天然重复,已在引擎侧豁免
- name: strip_repeated_short_lines
order: 20
params:
threshold: 3
max_len: 40
burst_limit: 5
burst_gap: 2
protect:
- "公章" # 投标人:(公章)
- "签名|签字|盖章" # 法定代表人签名:
- "日期|年.*月.*日" # 日期: / 日期: 年 月 日
- "^致[:]" # 致:xxx(投标函收件人)
- "负责|声明" # 声明函固定结尾句(中小企业声明函等)
# 3) 目录点线:"第一章 投标邀请函 ……………… 2"(保留标题文字,去掉点线和页码)
- name: strip_toc_dots
order: 30
# 4) 图片引用:![](images/xxx.jpg) 为死链,整行删除
- name: drop_images
order: 40
params:
placeholder: null # 需要保留位置时改为 "[图]" 之类
# 5) HTML 表格 → Markdown 管道表格(<table><tr><td> 单行压缩形态)
- name: normalize_tables
order: 50
# 6) 散落的 <br/> 标签
- name: strip_stray_html
order: 60
# 7) 行尾空白(MinerU 输出每行带双空格硬换行符)
- name: rstrip_lines
order: 70
# 8) 连续空行压为 1 行,裁掉文首文末空白
- name: collapse_blank_lines
order: 80
+154
View File
@@ -0,0 +1,154 @@
"""清洗规则单元测试。
fixtures 里是各脏数据模式的最小样例,跑一遍断言规则命中且正文无损。
"""
import sys
import unittest
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from cleaner.cleaner import MarkdownCleaner
from cleaner.rules import (
_drop_images,
_normalize_tables,
_rstrip_lines,
_strip_page_lines,
_strip_repeated_short_lines,
_strip_toc_dots,
load_rules,
)
S = {} # 每个用例独立 stats
def st():
return {}
class TestPageLines(unittest.TestCase):
def test_cn_page(self):
text = "正文A\n第 3 页 共 53 页\n正文B\n第4页共4页\n结尾"
out = _strip_page_lines(text, {}, st())
self.assertEqual(out, "正文A\n正文B\n结尾")
def test_en_page(self):
text = "foo\nPage 3 of 10\nbar\n- 4 -\nbaz"
out = _strip_page_lines(text, {"keep_bare_numbers": False}, st())
self.assertNotIn("Page 3", out)
self.assertNotIn("- 4 -", out)
def test_bare_number_kept_by_default(self):
text = "条款\n12\n下文"
out = _strip_page_lines(text, {}, st())
self.assertIn("12", out)
class TestRepeatedLines(unittest.TestCase):
def test_header_removed(self):
lines = ["某某采购项目招标文件"] + ["内容%d" % i for i in range(5)]
text = "\n".join(("某某采购项目招标文件 \n" + l) for l in lines)
out = _strip_repeated_short_lines(text, {"threshold": 3, "max_len": 40}, st())
self.assertNotIn("某某采购项目招标文件", out)
self.assertIn("内容1", out)
def test_protected_signature_kept(self):
text = "\n".join(["投标人:(公章)"] * 4 + ["正文"])
out = _strip_repeated_short_lines(
text, {"threshold": 3, "max_len": 40, "protect": ["公章"]}, st()
)
self.assertIn("投标人:(公章)", out)
def test_numbered_clause_kept(self):
# 编号条款是正文不是页眉
text = "\n".join(["1)乙方须接受甲方监督。"] * 4 + ["正文"])
out = _strip_repeated_short_lines(text, {"threshold": 3, "max_len": 40}, st())
self.assertIn("1)乙方须接受甲方监督。", out)
def test_ocr_burst_removed(self):
text = "\n".join(["审计程序"] * 30)
out = _strip_repeated_short_lines(
text,
{"threshold": 999, "max_len": 40, "burst_limit": 5, "burst_gap": 2},
st(),
)
self.assertNotIn("审计程序", out)
class TestTocDots(unittest.TestCase):
def test_toc_line(self):
text = "第一章 投标邀请函 ……………………………………… 2"
out = _strip_toc_dots(text, {}, st())
self.assertEqual(out, "第一章 投标邀请函")
def test_plain_toc_line_keeps_text(self):
# 无标题目录行:去掉点线页码,保留"序号+标题"文字
text = "21 迷交的投标文件 ………………………………………………… 25"
out = _strip_toc_dots(text, {}, st())
self.assertEqual(out, "21 迷交的投标文件")
def test_body_with_ellipsis_kept(self):
text = "此处省略部分内容……后续"
out = _strip_toc_dots(text, {}, st())
self.assertIn("后续", out)
class TestImages(unittest.TestCase):
def test_image_line_dropped(self):
text = "![](images/abc.jpg) \n正文"
out = _drop_images(text, {}, st())
self.assertEqual(out, "正文")
def test_placeholder(self):
text = "![](images/abc.jpg)\n正文"
out = _drop_images(text, {"placeholder": "[图]"}, st())
self.assertIn("[图]", out)
def test_base64_image_dropped(self):
text = "![名称](data:image/png;base64,AAAA)\n正文"
out = _drop_images(text, {}, st())
self.assertNotIn("base64", out)
class TestTables(unittest.TestCase):
def test_table_to_pipe(self):
html = "<table><tr><td>序号</td><td>名称</td></tr><tr><td>1</td><td>保洁</td></tr></table>"
out = _normalize_tables(html, {}, st())
self.assertIn("| 序号 | 名称 |", out)
self.assertIn("| 1 | 保洁 |", out)
self.assertIn("|---|---|", out)
def test_pipe_escaped(self):
html = "<table><tr><td>a|b</td></tr></table>"
out = _normalize_tables(html, {}, st())
self.assertIn("a\\|b", out)
def test_br_in_cell(self):
html = "<table><tr><td>品<br/>目</td></tr></table>"
out = _normalize_tables(html, {}, st())
self.assertIn("品 目", out)
class TestEngine(unittest.TestCase):
def test_full_pipeline(self):
cleaner = MarkdownCleaner(rules=load_rules())
raw = (
"# 标题\n![](images/x.jpg) \n第 1 页 共 2 页 \n"
"正文一段。 \n<table><tr><td>a</td></tr></table> \n\n\n\n尾部\n"
)
result = cleaner.clean_text(raw)
self.assertNotIn("images/", result.text)
self.assertNotIn("第 1 页", result.text)
self.assertNotIn("<table>", result.text)
self.assertIn("| a |", result.text)
self.assertNotIn("\n\n\n", result.text)
def test_default_rules_load(self):
rules = load_rules()
self.assertTrue(len(rules) >= 8)
self.assertEqual(rules, sorted(rules, key=lambda r: r.order))
if __name__ == "__main__":
unittest.main(verbosity=2)