Add Paper A (IEEE TAI) complete draft with Firm A-calibrated dual-method classification

Paper draft includes all sections (Abstract through Conclusion), 36 references,
and supporting scripts. Key methodology: Cosine similarity + dHash dual-method
verification with thresholds calibrated against known-replication firm (Firm A).

Includes:
- 8 section markdown files (paper_a_*.md)
- Ablation study script (ResNet-50 vs VGG-16 vs EfficientNet-B0)
- Recalibrated classification script (84,386 PDFs, 5-tier system)
- Figure generation and Word export scripts
- Citation renumbering script ([1]-[36])
- Signature analysis pipeline (12 steps)
- YOLO extraction scripts

Three rounds of AI review completed (GPT-5.4, Claude Opus 4.6, Gemini 3 Pro).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

signature_analysis/05_extract_names.py

@@ -0,0 +1,432 @@
+#!/usr/bin/env python3
+"""
+Step 5: 從 PDF 提取會計師印刷姓名
+
+流程：
+1. 從資料庫讀取簽名記錄，按 (PDF, page) 分組
+2. 對每個頁面重新執行 YOLO 獲取簽名框座標
+3. 對整頁執行 PaddleOCR 提取印刷文字
+4. 過濾出候選姓名（2-4 個中文字）
+5. 配對簽名與最近的印刷姓名
+6. 更新資料庫的 accountant_name 欄位
+"""
+
+import sqlite3
+import json
+import re
+import sys
+import time
+from pathlib import Path
+from typing import Optional, List, Dict, Tuple
+from collections import defaultdict
+from tqdm import tqdm
+import numpy as np
+import cv2
+import fitz  # PyMuPDF
+
+# 加入父目錄到路徑以便匯入
+sys.path.insert(0, str(Path(__file__).parent.parent))
+from paddleocr_client import PaddleOCRClient
+
+# 路徑配置
+PDF_BASE = Path("/Volumes/NV2/PDF-Processing/total-pdf")
+YOLO_MODEL_PATH = Path("/Volumes/NV2/pdf_recognize/models/best.pt")
+DB_PATH = Path("/Volumes/NV2/PDF-Processing/signature-analysis/signature_analysis.db")
+REPORTS_PATH = Path("/Volumes/NV2/PDF-Processing/signature-analysis/reports")
+
+# 處理配置
+DPI = 150
+CONFIDENCE_THRESHOLD = 0.5
+NAME_SEARCH_MARGIN = 200  # 簽名框周圍搜索姓名的像素範圍
+PROGRESS_SAVE_INTERVAL = 100  # 每處理 N 個頁面保存一次進度
+
+# 中文姓名正則
+CHINESE_NAME_PATTERN = re.compile(r'^[\u4e00-\u9fff]{2,4}$')
+
+
+def find_pdf_file(filename: str) -> Optional[str]:
+    """搜尋 PDF 檔案路徑"""
+    # 先在 batch_* 子目錄尋找
+    for batch_dir in sorted(PDF_BASE.glob("batch_*")):
+        pdf_path = batch_dir / filename
+        if pdf_path.exists():
+            return str(pdf_path)
+
+    # 再在頂層目錄尋找
+    pdf_path = PDF_BASE / filename
+    if pdf_path.exists():
+        return str(pdf_path)
+
+    return None
+
+
+def render_pdf_page(pdf_path: str, page_num: int) -> Optional[np.ndarray]:
+    """渲染 PDF 頁面為圖像"""
+    try:
+        doc = fitz.open(pdf_path)
+        if page_num < 1 or page_num > len(doc):
+            doc.close()
+            return None
+
+        page = doc[page_num - 1]
+        mat = fitz.Matrix(DPI / 72, DPI / 72)
+        pix = page.get_pixmap(matrix=mat, alpha=False)
+        image = np.frombuffer(pix.samples, dtype=np.uint8)
+        image = image.reshape(pix.height, pix.width, pix.n)
+        doc.close()
+        return image
+    except Exception as e:
+        print(f"渲染失敗: {pdf_path} page {page_num}: {e}")
+        return None
+
+
+def detect_signatures_yolo(image: np.ndarray, model) -> List[Dict]:
+    """使用 YOLO 偵測簽名框"""
+    results = model(image, conf=CONFIDENCE_THRESHOLD, verbose=False)
+
+    signatures = []
+    for r in results:
+        for box in r.boxes:
+            x1, y1, x2, y2 = map(int, box.xyxy[0].cpu().numpy())
+            conf = float(box.conf[0].cpu().numpy())
+            signatures.append({
+                'x': x1,
+                'y': y1,
+                'width': x2 - x1,
+                'height': y2 - y1,
+                'confidence': conf,
+                'center_x': (x1 + x2) / 2,
+                'center_y': (y1 + y2) / 2
+            })
+
+    # 按位置排序（上到下，左到右）
+    signatures.sort(key=lambda s: (s['y'], s['x']))
+
+    return signatures
+
+
+def extract_text_candidates(image: np.ndarray, ocr_client: PaddleOCRClient) -> List[Dict]:
+    """從圖像中提取所有文字候選"""
+    try:
+        results = ocr_client.ocr(image)
+
+        candidates = []
+        for result in results:
+            text = result.get('text', '').strip()
+            box = result.get('box', [])
+            confidence = result.get('confidence', 0.0)
+
+            if not box or not text:
+                continue
+
+            # 計算邊界框中心
+            xs = [point[0] for point in box]
+            ys = [point[1] for point in box]
+            center_x = sum(xs) / len(xs)
+            center_y = sum(ys) / len(ys)
+
+            candidates.append({
+                'text': text,
+                'center_x': center_x,
+                'center_y': center_y,
+                'x': min(xs),
+                'y': min(ys),
+                'width': max(xs) - min(xs),
+                'height': max(ys) - min(ys),
+                'confidence': confidence
+            })
+
+        return candidates
+    except Exception as e:
+        print(f"OCR 失敗: {e}")
+        return []
+
+
+def filter_name_candidates(candidates: List[Dict]) -> List[Dict]:
+    """過濾出可能是姓名的文字（2-4 個中文字，不含數字標點）"""
+    names = []
+    for c in candidates:
+        text = c['text']
+        # 移除空白和標點
+        text_clean = re.sub(r'[\s\:\：\,\，\.\。]', '', text)
+
+        if CHINESE_NAME_PATTERN.match(text_clean):
+            c['text_clean'] = text_clean
+            names.append(c)
+
+    return names
+
+
+def match_signature_to_name(
+    sig: Dict,
+    name_candidates: List[Dict],
+    margin: int = NAME_SEARCH_MARGIN
+) -> Optional[str]:
+    """為簽名框配對最近的姓名候選"""
+    sig_center_x = sig['center_x']
+    sig_center_y = sig['center_y']
+
+    # 過濾出在搜索範圍內的姓名
+    nearby_names = []
+    for name in name_candidates:
+        dx = abs(name['center_x'] - sig_center_x)
+        dy = abs(name['center_y'] - sig_center_y)
+
+        # 在 margin 範圍內
+        if dx <= margin + sig['width']/2 and dy <= margin + sig['height']/2:
+            distance = (dx**2 + dy**2) ** 0.5
+            nearby_names.append((name, distance))
+
+    if not nearby_names:
+        return None
+
+    # 返回距離最近的
+    nearby_names.sort(key=lambda x: x[1])
+    return nearby_names[0][0]['text_clean']
+
+
+def get_pages_to_process(conn: sqlite3.Connection) -> List[Tuple[str, int, List[int]]]:
+    """
+    從資料庫獲取需要處理的 (PDF, page) 組合
+
+    Returns:
+        List of (source_pdf, page_number, [signature_ids])
+    """
+    cursor = conn.cursor()
+
+    # 查詢尚未有 accountant_name 的簽名，按 (PDF, page) 分組
+    cursor.execute('''
+        SELECT source_pdf, page_number, GROUP_CONCAT(signature_id)
+        FROM signatures
+        WHERE accountant_name IS NULL OR accountant_name = ''
+        GROUP BY source_pdf, page_number
+        ORDER BY source_pdf, page_number
+    ''')
+
+    pages = []
+    for row in cursor.fetchall():
+        source_pdf, page_number, sig_ids_str = row
+        sig_ids = [int(x) for x in sig_ids_str.split(',')]
+        pages.append((source_pdf, page_number, sig_ids))
+
+    return pages
+
+
+def update_signature_names(
+    conn: sqlite3.Connection,
+    updates: List[Tuple[int, str, int, int, int, int]]
+):
+    """
+    更新資料庫中的簽名姓名和座標
+
+    Args:
+        updates: List of (signature_id, accountant_name, x, y, width, height)
+    """
+    cursor = conn.cursor()
+
+    # 確保 signature_boxes 表存在
+    cursor.execute('''
+        CREATE TABLE IF NOT EXISTS signature_boxes (
+            signature_id INTEGER PRIMARY KEY,
+            x INTEGER,
+            y INTEGER,
+            width INTEGER,
+            height INTEGER,
+            FOREIGN KEY (signature_id) REFERENCES signatures(signature_id)
+        )
+    ''')
+
+    for sig_id, name, x, y, w, h in updates:
+        # 更新姓名
+        cursor.execute('''
+            UPDATE signatures SET accountant_name = ? WHERE signature_id = ?
+        ''', (name, sig_id))
+
+        # 更新或插入座標
+        cursor.execute('''
+            INSERT OR REPLACE INTO signature_boxes (signature_id, x, y, width, height)
+            VALUES (?, ?, ?, ?, ?)
+        ''', (sig_id, x, y, w, h))
+
+    conn.commit()
+
+
+def process_page(
+    source_pdf: str,
+    page_number: int,
+    sig_ids: List[int],
+    yolo_model,
+    ocr_client: PaddleOCRClient,
+    conn: sqlite3.Connection
+) -> Dict:
+    """
+    處理單一頁面：偵測簽名框、提取姓名、配對
+
+    Returns:
+        處理結果統計
+    """
+    result = {
+        'source_pdf': source_pdf,
+        'page_number': page_number,
+        'num_signatures': len(sig_ids),
+        'matched': 0,
+        'unmatched': 0,
+        'error': None
+    }
+
+    # 找 PDF 檔案
+    pdf_path = find_pdf_file(source_pdf)
+    if pdf_path is None:
+        result['error'] = 'PDF not found'
+        return result
+
+    # 渲染頁面
+    image = render_pdf_page(pdf_path, page_number)
+    if image is None:
+        result['error'] = 'Render failed'
+        return result
+
+    # YOLO 偵測簽名框
+    sig_boxes = detect_signatures_yolo(image, yolo_model)
+
+    if len(sig_boxes) != len(sig_ids):
+        # 簽名數量不匹配，嘗試按順序配對
+        pass
+
+    # OCR 提取文字
+    text_candidates = extract_text_candidates(image, ocr_client)
+
+    # 過濾出姓名候選
+    name_candidates = filter_name_candidates(text_candidates)
+
+    # 配對簽名與姓名
+    updates = []
+
+    for i, (sig_id, sig_box) in enumerate(zip(sig_ids, sig_boxes)):
+        matched_name = match_signature_to_name(sig_box, name_candidates)
+
+        if matched_name:
+            result['matched'] += 1
+        else:
+            result['unmatched'] += 1
+            matched_name = ''  # 空字串表示未配對
+
+        updates.append((
+            sig_id,
+            matched_name,
+            sig_box['x'],
+            sig_box['y'],
+            sig_box['width'],
+            sig_box['height']
+        ))
+
+    # 如果 YOLO 偵測數量少於記錄數量，處理剩餘的
+    if len(sig_boxes) < len(sig_ids):
+        for sig_id in sig_ids[len(sig_boxes):]:
+            updates.append((sig_id, '', 0, 0, 0, 0))
+            result['unmatched'] += 1
+
+    # 更新資料庫
+    update_signature_names(conn, updates)
+
+    return result
+
+
+def main():
+    print("=" * 60)
+    print("Step 5: 從 PDF 提取會計師印刷姓名")
+    print("=" * 60)
+
+    # 確保報告目錄存在
+    REPORTS_PATH.mkdir(parents=True, exist_ok=True)
+
+    # 連接資料庫
+    print("\n連接資料庫...")
+    conn = sqlite3.connect(DB_PATH)
+
+    # 獲取需要處理的頁面
+    print("查詢待處理頁面...")
+    pages = get_pages_to_process(conn)
+    print(f"共 {len(pages)} 個頁面待處理")
+
+    if not pages:
+        print("沒有需要處理的頁面")
+        conn.close()
+        return
+
+    # 初始化 YOLO
+    print("\n載入 YOLO 模型...")
+    from ultralytics import YOLO
+    yolo_model = YOLO(str(YOLO_MODEL_PATH))
+
+    # 初始化 OCR 客戶端
+    print("連接 PaddleOCR 伺服器...")
+    ocr_client = PaddleOCRClient()
+    if not ocr_client.health_check():
+        print("錯誤: PaddleOCR 伺服器無法連接")
+        print("請確認伺服器 http://192.168.30.36:5555 正在運行")
+        conn.close()
+        return
+    print("OCR 伺服器連接成功")
+
+    # 統計
+    stats = {
+        'total_pages': len(pages),
+        'processed': 0,
+        'matched': 0,
+        'unmatched': 0,
+        'errors': 0,
+        'start_time': time.time()
+    }
+
+    # 處理每個頁面
+    print(f"\n開始處理 {len(pages)} 個頁面...")
+
+    for source_pdf, page_number, sig_ids in tqdm(pages, desc="處理頁面"):
+        result = process_page(
+            source_pdf, page_number, sig_ids,
+            yolo_model, ocr_client, conn
+        )
+
+        stats['processed'] += 1
+        stats['matched'] += result['matched']
+        stats['unmatched'] += result['unmatched']
+        if result['error']:
+            stats['errors'] += 1
+
+        # 定期保存進度報告
+        if stats['processed'] % PROGRESS_SAVE_INTERVAL == 0:
+            elapsed = time.time() - stats['start_time']
+            rate = stats['processed'] / elapsed
+            remaining = (stats['total_pages'] - stats['processed']) / rate if rate > 0 else 0
+
+            print(f"\n進度: {stats['processed']}/{stats['total_pages']} "
+                  f"({stats['processed']/stats['total_pages']*100:.1f}%)")
+            print(f"配對成功: {stats['matched']}, 未配對: {stats['unmatched']}")
+            print(f"預估剩餘時間: {remaining/60:.1f} 分鐘")
+
+    # 最終統計
+    elapsed = time.time() - stats['start_time']
+    stats['elapsed_seconds'] = elapsed
+
+    print("\n" + "=" * 60)
+    print("處理完成")
+    print("=" * 60)
+    print(f"總頁面數: {stats['total_pages']}")
+    print(f"處理成功: {stats['processed']}")
+    print(f"配對成功: {stats['matched']}")
+    print(f"未配對: {stats['unmatched']}")
+    print(f"錯誤: {stats['errors']}")
+    print(f"耗時: {elapsed/60:.1f} 分鐘")
+
+    # 保存報告
+    report_path = REPORTS_PATH / "name_extraction_report.json"
+    with open(report_path, 'w', encoding='utf-8') as f:
+        json.dump(stats, f, indent=2, ensure_ascii=False)
+    print(f"\n報告已儲存: {report_path}")
+
+    conn.close()
+
+
+if __name__ == "__main__":
+    main()