feat: initial commit for post-ocr extraction pipeline

src/processor.py

@@ -0,0 +1,87 @@
+import re
+import pandas as pd
+from typing import List, Dict, Any
+from pydantic import BaseModel, Field
+
+
+class EnvelopeRecord(BaseModel):
+    编号: str = ""
+    邮编: str = ""
+    地址: str = ""
+    联系人_单位名: str = Field(default="", alias="联系人/单位名")
+    电话: str = ""
+
+
+def clean_text(text: str) -> str:
+    """清理OCR识别出的杂质字符"""
+    return text.strip().replace(" ", "")
+
+
+def extract_info(ocr_results: List[str]) -> Dict[str, str]:
+    """
+    从OCR结果列表中提取结构化信息。
+    """
+    data = {"编号": "", "邮编": "", "地址": "", "联系人/单位名": "", "电话": ""}
+
+    full_content = " ".join(ocr_results)
+
+    # 1. 提取邮编 (6位数字)
+    zip_match = re.search(r"\b(\d{6})\b", full_content)
+    if zip_match:
+        data["邮编"] = zip_match.group(1)
+
+    # 2. 提取电话 (11位手机号或带区号固话)
+    phone_match = re.search(r"(1[3-9]\d{9}|0\d{2,3}-\d{7,8})", full_content)
+    if phone_match:
+        data["电话"] = phone_match.group(0)
+
+    # 3. 提取联系人 (通常在电话前面，或者是独立的短行)
+    # 遍历每一行寻找包含电话的行
+    for line in ocr_results:
+        if data["电话"] and data["电话"] in line:
+            # 移除电话部分，剩下的可能是姓名
+            name_part = line.replace(data["电话"], "").strip()
+            # 进一步清洗姓名（移除符号）
+            name_part = re.sub(r"[^\w\u4e00-\u9fa5]", "", name_part)
+            if name_part:
+                data["联系人/单位名"] = name_part
+            break
+
+    # 如果还没找到联系人，尝试找不含数字的短行
+    if not data["联系人/单位名"]:
+        for line in ocr_results:
+            clean_line = re.sub(r"[^\w\u4e00-\u9fa5]", "", line)
+            if 2 <= len(clean_line) <= 10 and not re.search(r"\d", clean_line):
+                data["联系人/单位名"] = clean_line
+                break
+
+    # 4. 提取地址
+    address_match = re.search(
+        r"([^,，。\s]*(?:省|市|区|县|乡|镇|路|街|村|组|号)[^,，。\s]*)", full_content
+    )
+    if address_match:
+        data["地址"] = address_match.group(1)
+    else:
+        # 兜底：寻找较长的包含地名特征的行
+        for line in ocr_results:
+            if any(k in line for k in ["省", "市", "区", "县", "乡", "镇", "村"]):
+                data["地址"] = line.strip()
+                break
+
+    # 5. 提取编号 (长数字串)
+    # 排除邮编和电话后的最长数字串
+    long_numbers = re.findall(r"\b\d{10,20}\b", full_content)
+    for num in long_numbers:
+        if num != data["电话"]:
+            data["编号"] = num
+            break
+
+    return data
+
+
+def save_to_excel(records: List[Dict[str, Any]], output_path: str):
+    df = pd.DataFrame(records)
+    # 调整列顺序
+    cols = ["编号", "邮编", "地址", "联系人/单位名", "电话"]
+    df = df.reindex(columns=cols)
+    df.to_excel(output_path, index=False)