feat(ocr): 集成精准表格 OCR 到文档提取流程

- PdfStructureExtractor 支持 use_ocr 参数
- 版面分析检测表格区域坐标
- 仅 OCR 表格区域，保留其他文本格式
- 章节标题等非表格内容保持 PyMuPDF 提取

core/construction_review/component/doc_worker/pdf_worker/ocr_enhanced_extractor.py

@@ -1,16 +1,16 @@
 """
-OCR 增强提取器 - 稳定版
+OCR 增强提取器 - 精准表格区域版
 
 流程：
-1. PyMuPDF 提取全部文本（用于章节切分）
-2. RapidLayout 检测表格页
-3. 对表格页 OCR，替换该页内容
-4. 保持章节切分逻辑不变
+1. PyMuPDF 提取全部文本（用于章节切分，确保格式稳定）
+2. RapidLayout 检测表格区域（返回坐标）
+3. 只对表格区域进行 OCR，替换该区域内容
+4. 其他文本保持 PyMuPDF 提取结果，章节标题不受影响
 
 特点：
 - 章节切分基于 PyMuPDF 文本（格式稳定，正则匹配可靠）
-- 表格页内容通过 OCR 补充（识别率高）
-- 输出标记哪些页使用了 OCR
+- 仅表格区域使用 OCR（精准定位，不影响其他内容）
+- 输出标记哪些页使用了 OCR 及表格区域坐标
 """
 
 from __future__ import annotations
@@ -124,61 +124,69 @@ class OcrEnhancedExtractor(FullTextExtractor):
             self._layout_engine = RapidLayout()
         return self._layout_engine
 
-    def _detect_table_pages(self, doc: fitz.Document) -> Set[int]:
-        """检测含表格的页码"""
-        table_pages: Set[int] = set()
+    def _detect_table_regions(self, page: fitz.Page, page_num: int) -> List[Tuple[Tuple[float, float, float, float], float]]:
+        """
+        检测页面中的表格区域
+
+        Args:
+            page: PDF 页面对象
+            page_num: 页码（用于日志）
+
+        Returns:
+            表格区域列表，每个元素为 ((x1, y1, x2, y2), score)
+        """
+        table_regions: List[Tuple[Tuple[float, float, float, float], float]] = []
 
         if not RAPID_LAYOUT_AVAILABLE:
-            return table_pages
+            return table_regions
 
         layout_engine = self._get_layout_engine()
         if layout_engine is None:
-            return table_pages
+            return table_regions
 
-        logger.info(f"[版面分析] 检测表格页，共 {len(doc)} 页")
-
-        for page_num in range(1, len(doc) + 1):
-            page = doc[page_num - 1]
-
-            # 裁剪页眉页脚
-            rect = page.rect
-            clip_box = fitz.Rect(0, self.clip_top, rect.width, rect.height - self.clip_bottom)
-
-            # 渲染页面
-            pix = page.get_pixmap(dpi=self.dpi, clip=clip_box)
-            img = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.height, pix.width, 3)
-
-            try:
-                layout_output = layout_engine(img)
+        # 裁剪页眉页脚
+        rect = page.rect
+        clip_box = fitz.Rect(0, self.clip_top, rect.width, rect.height - self.clip_bottom)
 
-                # 解析版面结果
-                labels = []
-                if hasattr(layout_output, 'class_names'):
-                    labels = list(layout_output.class_names)
-                elif hasattr(layout_output, 'boxes'):
-                    labels = [
-                        label for _, label, _
-                        in zip(layout_output.boxes, layout_output.class_names, layout_output.scores)
-                    ]
+        # 渲染页面
+        pix = page.get_pixmap(dpi=self.dpi, clip=clip_box)
+        img = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.height, pix.width, 3)
 
-                if "table" in labels:
-                    table_pages.add(page_num)
-                    logger.debug(f"  第 {page_num} 页: 检测到表格")
+        try:
+            layout_output = layout_engine(img)
+
+            # 解析版面结果
+            if hasattr(layout_output, 'boxes') and hasattr(layout_output, 'class_names'):
+                # 获取缩放比例（像素坐标转 PDF 坐标）
+                scale_x = clip_box.width / img.shape[1]
+                scale_y = clip_box.height / img.shape[0]
+
+                for box, label, score in zip(layout_output.boxes, layout_output.class_names, layout_output.scores):
+                    if label == "table" and score > 0.5:  # 置信度阈值
+                        # box 格式: [x1, y1, x2, y2] 像素坐标
+                        # 转换为 PDF 坐标（加上裁剪区域的偏移）
+                        pdf_x1 = clip_box.x0 + box[0] * scale_x
+                        pdf_y1 = clip_box.y0 + box[1] * scale_y
+                        pdf_x2 = clip_box.x0 + box[2] * scale_x
+                        pdf_y2 = clip_box.y0 + box[3] * scale_y
+
+                        table_regions.append(((pdf_x1, pdf_y1, pdf_x2, pdf_y2), score))
+                        logger.debug(f"  第 {page_num} 页: 检测到表格 ({pdf_x1:.1f}, {pdf_y1:.1f}, {pdf_x2:.1f}, {pdf_y2:.1f}), 置信度 {score:.2f}")
 
-            except Exception as e:
-                logger.warning(f"  第 {page_num} 页: 版面分析失败 ({e})")
+        except Exception as e:
+            logger.warning(f"  第 {page_num} 页: 版面分析失败 ({e})")
 
-        logger.info(f"[版面分析] 检测到 {len(table_pages)} 页含表格")
-        return table_pages
+        return table_regions
 
     def extract_full_text(self, source: DocumentSource) -> List[Dict[str, Any]]:
         """
-        执行 OCR 增强提取
+        执行 OCR 增强提取（精准表格区域版）
 
         流程：
-        1. PyMuPDF 提取全部文本
-        2. 检测表格页
-        3. 对表格页 OCR 替换内容
+        1. PyMuPDF 提取全部文本（确保章节格式稳定）
+        2. 检测每页的表格区域（返回坐标）
+        3. 只 OCR 表格区域，替换该区域内容
+        4. 其他文本保持 PyMuPDF 结果
         """
         total_start = time.time()
 
@@ -211,44 +219,69 @@ class OcrEnhancedExtractor(FullTextExtractor):
                     "start_pos": 0,  # 后续计算
                     "end_pos": 0,
                     "source_file": source_file,
-                    "is_ocr": False,  # 标记是否 OCR
+                    "is_ocr": False,
+                    "ocr_regions": [],  # OCR 区域信息
                 })
 
-            # 阶段 2: 检测表格页
-            logger.info("[阶段2] 检测表格页...")
-            table_pages = self._detect_table_pages(doc)
+            # 阶段 2&3: 逐页检测表格区域并 OCR 替换
+            logger.info("[阶段2] 检测表格区域并精准 OCR...")
+            total_ocr_count = 0
+            total_ocr_time = 0.0
+
+            for page_num in range(1, total_pages + 1):
+                page = doc[page_num - 1]
+
+                # 检测该页的表格区域
+                table_regions = self._detect_table_regions(page, page_num)
 
-            # 阶段 3: 对表格页 OCR
-            if table_pages:
-                logger.info(f"[阶段3] 对 {len(table_pages)} 页进行 OCR...")
-                ocr_count = 0
-                ocr_time = 0.0
+                if not table_regions:
+                    continue
 
-                for page_num in table_pages:
-                    page = doc[page_num - 1]
+                logger.info(f"  第 {page_num} 页: 检测到 {len(table_regions)} 个表格区域")
 
+                # 对每个表格区域进行 OCR
+                ocr_results = []
+                for idx, (bbox, score) in enumerate(table_regions):
                     try:
                         ocr_start = time.time()
 
-                        if self.ocr_engine_normalized == "glm_ocr":
-                            ocr_text = self._ocr_with_glm(page, page_num)
-                        else:
-                            ocr_text = self._ocr_with_mineru(doc, page_num)
+                        # 只 OCR 表格区域
+                        ocr_text = self._ocr_table_region(page, bbox)
 
-                        ocr_time += time.time() - ocr_start
-                        ocr_count += 1
+                        ocr_time = time.time() - ocr_start
+                        total_ocr_time += ocr_time
 
-                        # 替换该页内容
-                        pages[page_num - 1]["text"] = ocr_text
-                        pages[page_num - 1]["is_ocr"] = True
-                        pages[page_num - 1]["original_text"] = pages[page_num - 1]["text"]  # 保留原文
+                        ocr_results.append({
+                            "region_index": idx,
+                            "bbox": bbox,
+                            "score": score,
+                            "ocr_text": ocr_text,
+                            "ocr_time": ocr_time,
+                        })
 
-                        logger.debug(f"  第 {page_num} 页: OCR 完成 ({len(ocr_text)} 字符)")
+                        logger.debug(f"    区域 {idx+1}: OCR 完成 ({len(ocr_text)} 字符), 耗时 {ocr_time:.2f}s")
 
                     except Exception as e:
-                        logger.error(f"  第 {page_num} 页: OCR 失败 ({e})，使用原文")
+                        logger.error(f"    区域 {idx+1}: OCR 失败 ({e})，保留原文")
+
+                # 替换表格区域内容
+                if ocr_results:
+                    original_text = pages[page_num - 1]["text"]
+                    updated_text = self._replace_table_regions(
+                        page, original_text, ocr_results, table_regions
+                    )
+
+                    pages[page_num - 1]["text"] = updated_text
+                    pages[page_num - 1]["is_ocr"] = True
+                    pages[page_num - 1]["ocr_regions"] = [
+                        {"bbox": r["bbox"], "score": r["score"], "chars": len(r["ocr_text"])}
+                        for r in ocr_results
+                    ]
+
+                    total_ocr_count += len(ocr_results)
 
-                logger.info(f"[OCR] 完成 {ocr_count} 页，耗时 {ocr_time:.2f}s")
+            if total_ocr_count > 0:
+                logger.info(f"[OCR] 完成 {total_ocr_count} 个表格区域，耗时 {total_ocr_time:.2f}s")
 
             # 阶段 4: 计算位置
             current_pos = 0
@@ -264,19 +297,186 @@ class OcrEnhancedExtractor(FullTextExtractor):
         # 统计
         total_time = time.time() - total_start
         ocr_pages = sum(1 for p in pages if p.get("is_ocr"))
+        total_ocr_regions = sum(len(p.get("ocr_regions", [])) for p in pages)
         total_chars = sum(len(p["text"]) for p in pages)
 
         logger.info(
             f"[提取完成] 总页数: {total_pages} | "
-            f"OCR: {ocr_pages} | 本地: {total_pages - ocr_pages} | "
+            f"OCR页: {ocr_pages} | 本地页: {total_pages - ocr_pages} | "
+            f"OCR区域: {total_ocr_regions} | "
             f"总耗时: {total_time:.2f}s | "
             f"总字符: {total_chars}"
         )
 
         return pages
 
+    def _ocr_table_region(self, page: fitz.Page, bbox: Tuple[float, float, float, float]) -> str:
+        """
+        对指定区域进行 OCR 识别
+
+        Args:
+            page: PDF 页面对象
+            bbox: 区域坐标 (x1, y1, x2, y2)
+
+        Returns:
+            OCR 识别结果文本
+        """
+        # 渲染指定区域
+        rect = fitz.Rect(bbox)
+        pix = page.get_pixmap(dpi=self.dpi, clip=rect)
+        img_bytes = pix.tobytes("jpeg")
+
+        # 压缩
+        compressed = self._compress_image(img_bytes)
+        img_base64 = base64.b64encode(compressed).decode('utf-8')
+
+        # 请求 OCR
+        payload = {
+            "model": "GLM-OCR",
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": "识别图片中的表格内容，按原文排版输出。"
+                                    "注意："
+                                    "1. 表格用 Markdown 表格格式"
+                                    "2. 保持换行和列对齐"
+                                    "3. 只输出表格内容，不要其他说明"
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": f"data:image/jpeg;base64,{img_base64}"}
+                        }
+                    ]
+                }
+            ],
+            "max_tokens": 2048,
+            "temperature": 0.1
+        }
+
+        response = requests.post(
+            self.glm_api_url,
+            headers=self.glm_headers,
+            json=payload,
+            timeout=self.glm_timeout
+        )
+        response.raise_for_status()
+
+        result = response.json()
+        content = self._extract_content(result)
+
+        return content
+
+    def _replace_table_regions(
+        self,
+        page: fitz.Page,
+        original_text: str,
+        ocr_results: List[Dict[str, Any]],
+        table_regions: List[Tuple[Tuple[float, float, float, float], float]]
+    ) -> str:
+        """
+        用 OCR 结果替换原始文本中的表格区域
+
+        策略：
+        1. 找到表格区域在原始文本中的位置
+        2. 用 OCR 结果替换该部分内容
+        3. 保留其他所有文本（包括章节标题）
+
+        Args:
+            page: PDF 页面对象
+            original_text: 原始文本（PyMuPDF 提取）
+            ocr_results: OCR 结果列表
+            table_regions: 表格区域坐标列表
+
+        Returns:
+            替换后的文本
+        """
+        if not ocr_results:
+            return original_text
+
+        # 获取页面上的文本块及其坐标
+        text_blocks = []
+        for block in page.get_text("blocks"):
+            # block 格式: (x0, y0, x1, y1, text, block_no, block_type)
+            x0, y0, x1, y1, text, _, _ = block
+            # 只考虑页眉页脚裁剪区域内的文本
+            if y0 >= self.clip_top and y1 <= page.rect.height - self.clip_bottom:
+                text_blocks.append({
+                    "bbox": (x0, y0, x1, y1),
+                    "text": text.strip(),
+                })
+
+        # 按 Y 坐标排序（从上到下）
+        text_blocks.sort(key=lambda b: (b["bbox"][1], b["bbox"][0]))
+
+        # 标记哪些文本块属于表格区域
+        replaced_indices = set()
+        for region_idx, (bbox, _) in enumerate(table_regions):
+            for idx, block in enumerate(text_blocks):
+                if idx in replaced_indices:
+                    continue
+                # 检查文本块是否与表格区域有重叠
+                bx0, by0, bx1, by1 = block["bbox"]
+                rx0, ry0, rx1, ry1 = bbox
+
+                # 计算重叠区域
+                overlap_x = max(0, min(bx1, rx1) - max(bx0, rx0))
+                overlap_y = max(0, min(by1, ry1) - max(by0, ry0))
+                overlap_area = overlap_x * overlap_y
+                block_area = (bx1 - bx0) * (by1 - by0)
+
+                # 如果重叠面积超过 50%，认为是表格内的文本
+                if block_area > 0 and overlap_area / block_area > 0.5:
+                    replaced_indices.add(idx)
+
+        # 构建新文本：保留非表格区域的文本，替换表格区域为 OCR 结果
+        result_parts = []
+        last_idx = 0
+
+        # 按顺序处理每个表格区域
+        for region_idx, (bbox, score) in enumerate(table_regions):
+            if region_idx >= len(ocr_results):
+                continue
+
+            ocr_text = ocr_results[region_idx]["ocr_text"]
+
+            # 找到该表格区域之前需要保留的文本
+            region_blocks = []
+            for idx, block in enumerate(text_blocks):
+                if idx in replaced_indices:
+                    bx0, by0, bx1, by1 = block["bbox"]
+                    rx0, ry0, rx1, ry1 = bbox
+                    # 如果该文本块属于当前表格区域
+                    if (bx0 >= rx0 - 5 and bx1 <= rx1 + 5 and
+                        by0 >= ry0 - 5 and by1 <= ry1 + 5):
+                        region_blocks.append((idx, block))
+
+            if region_blocks:
+                # 在第一个表格块之前添加之前的内容
+                first_idx = region_blocks[0][0]
+                for idx in range(last_idx, first_idx):
+                    if idx not in replaced_indices:
+                        result_parts.append(text_blocks[idx]["text"])
+                        result_parts.append("\n")
+
+                # 添加 OCR 结果
+                result_parts.append(ocr_text)
+                result_parts.append("\n")
+
+                last_idx = region_blocks[-1][0] + 1
+
+        # 添加剩余的非表格文本
+        for idx in range(last_idx, len(text_blocks)):
+            if idx not in replaced_indices:
+                result_parts.append(text_blocks[idx]["text"])
+                result_parts.append("\n")
+
+        return "".join(result_parts)
+
     def _ocr_with_glm(self, page: fitz.Page, page_num: int) -> str:
-        """GLM-OCR 识别"""
+        """GLM-OCR 识别（整页版本，保留用于兼容）"""
         # 渲染页面
         rect = page.rect
         clip_box = fitz.Rect(0, self.clip_top, rect.width, rect.height - self.clip_bottom)

core/construction_review/component/document_processor.py

@@ -184,7 +184,7 @@ class DocumentProcessor:
                     stage=stage, current=current, message=message
                 )
 
-            simple_processor = SimpleDocumentProcessor()
+            simple_processor = SimpleDocumentProcessor(use_ocr=self.use_ocr)
             unified_doc = await simple_processor.process_unified(
                 file_content=file_content,
                 file_name=f"document_{uuid.uuid4().hex[:8]}",

core/construction_review/component/minimal_pipeline/pdf_extractor.py

@@ -2,27 +2,68 @@
 PDF 结构提取器
 
 基于 splitter_pdf 逻辑，直接提取章节结构并记录页码。
+支持 OCR 增强：检测表格区域并精准 OCR，其他文本保持 PyMuPDF 提取。
 输出格式兼容后续分类与组装流程。
 """
 
+import base64
+import io
 import re
-from typing import Dict, Any
+from typing import Dict, Any, List, Optional, Tuple, Set
 
 import fitz
+import numpy as np
+import requests
 
 from foundation.observability.logger.loggering import review_logger as logger
 
+# 尝试导入 RapidLayout
+try:
+    from rapid_layout import RapidLayout
+    RAPID_LAYOUT_AVAILABLE = True
+except ImportError:
+    RAPID_LAYOUT_AVAILABLE = False
+    RapidLayout = None
+
 
 class PdfStructureExtractor:
-    """PDF 章节结构提取器"""
+    """PDF 章节结构提取器（支持 OCR 增强）"""
 
     CHAPTER_PATTERN = re.compile(r"^第[一二三四五六七八九十百]+章\s*.*")
     SECTION_PATTERN = re.compile(r"^[一二三四五六七八九十百]+、\s*.*")
     TOC_PATTERN = re.compile(r"\.{3,}|…{2,}")
 
-    def __init__(self, clip_top: float = 60, clip_bottom: float = 60):
+    # OCR 配置
+    MAX_SHORT_EDGE = 1024
+    JPEG_QUALITY = 90
+    OCR_DPI = 200
+    OCR_CONFIDENCE_THRESHOLD = 0.5
+
+    def __init__(
+        self,
+        clip_top: float = 60,
+        clip_bottom: float = 60,
+        use_ocr: bool = False,
+        ocr_api_url: str = "http://183.220.37.46:25429/v1/chat/completions",
+        ocr_timeout: int = 600,
+    ):
         self.clip_top = clip_top
         self.clip_bottom = clip_bottom
+        self.use_ocr = use_ocr and RAPID_LAYOUT_AVAILABLE
+
+        # OCR 配置
+        self.ocr_api_url = ocr_api_url
+        self.ocr_timeout = ocr_timeout
+        self._layout_engine: Optional[Any] = None
+
+        if use_ocr and not RAPID_LAYOUT_AVAILABLE:
+            logger.warning("RapidLayout 未安装，OCR 功能不可用")
+
+    def _get_layout_engine(self) -> Optional[Any]:
+        """延迟初始化 RapidLayout"""
+        if self._layout_engine is None and RAPID_LAYOUT_AVAILABLE:
+            self._layout_engine = RapidLayout()
+        return self._layout_engine
 
     def extract(self, file_content: bytes) -> Dict[str, Any]:
         """
@@ -57,7 +98,13 @@ class PdfStructureExtractor:
             page = doc.load_page(page_num)
             rect = page.rect
             clip_box = fitz.Rect(0, self.clip_top, rect.width, rect.height - self.clip_bottom)
-            text = page.get_text("text", clip=clip_box)
+
+            # 获取页面文本（支持 OCR 增强）
+            if self.use_ocr:
+                text = self._extract_page_with_ocr(page, page_num + 1, clip_box)
+            else:
+                text = page.get_text("text", clip=clip_box)
+
             lines = text.split("\n")
 
             for line in lines:
@@ -133,6 +180,271 @@ class PdfStructureExtractor:
         logger.info(f"[PdfExtractor] 提取完成，共 {len(result['chapters'])} 个章节")
         return result
 
+    def _extract_page_with_ocr(self, page: fitz.Page, page_num: int, clip_box: fitz.Rect) -> str:
+        """
+        提取页面内容，对表格区域使用 OCR
+
+        流程：
+        1. PyMuPDF 提取全部文本（确保章节格式稳定）
+        2. 检测表格区域坐标
+        3. 只 OCR 表格区域
+        4. 用 OCR 结果替换表格区域内容
+        """
+        import time
+
+        start_time = time.time()
+
+        # 1. PyMuPDF 提取全部文本
+        original_text = page.get_text("text", clip=clip_box)
+
+        # 2. 检测表格区域
+        table_regions = self._detect_table_regions(page, page_num, clip_box)
+
+        if not table_regions:
+            return original_text
+
+        logger.info(f"  第 {page_num} 页: 检测到 {len(table_regions)} 个表格区域")
+
+        # 3. OCR 每个表格区域
+        ocr_results = []
+        for idx, (bbox, score) in enumerate(table_regions):
+            try:
+                ocr_text = self._ocr_table_region(page, bbox)
+                ocr_results.append({
+                    "region_index": idx,
+                    "bbox": bbox,
+                    "score": score,
+                    "ocr_text": ocr_text,
+                })
+                logger.debug(f"    区域 {idx+1}: OCR 完成 ({len(ocr_text)} 字符)")
+            except Exception as e:
+                logger.error(f"    区域 {idx+1}: OCR 失败 ({e})")
+
+        # 4. 替换表格区域内容
+        if ocr_results:
+            updated_text = self._replace_table_regions(
+                page, original_text, ocr_results, clip_box
+            )
+            elapsed = time.time() - start_time
+            logger.info(f"  第 {page_num} 页: OCR 处理完成，耗时 {elapsed:.2f}s")
+            return updated_text
+
+        return original_text
+
+    def _detect_table_regions(
+        self,
+        page: fitz.Page,
+        page_num: int,
+        clip_box: fitz.Rect
+    ) -> List[Tuple[Tuple[float, float, float, float], float]]:
+        """检测页面中的表格区域，返回坐标列表"""
+        table_regions: List[Tuple[Tuple[float, float, float, float], float]] = []
+
+        if not RAPID_LAYOUT_AVAILABLE:
+            return table_regions
+
+        layout_engine = self._get_layout_engine()
+        if layout_engine is None:
+            return table_regions
+
+        # 渲染页面（裁剪区域）
+        pix = page.get_pixmap(dpi=self.OCR_DPI, clip=clip_box)
+        img = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.height, pix.width, 3)
+
+        try:
+            layout_output = layout_engine(img)
+
+            # 解析版面结果
+            if hasattr(layout_output, 'boxes') and hasattr(layout_output, 'class_names'):
+                # 获取缩放比例
+                scale_x = clip_box.width / img.shape[1]
+                scale_y = clip_box.height / img.shape[0]
+
+                for box, label, score in zip(layout_output.boxes, layout_output.class_names, layout_output.scores):
+                    if label == "table" and score > self.OCR_CONFIDENCE_THRESHOLD:
+                        # 转换为 PDF 坐标
+                        pdf_x1 = clip_box.x0 + box[0] * scale_x
+                        pdf_y1 = clip_box.y0 + box[1] * scale_y
+                        pdf_x2 = clip_box.x0 + box[2] * scale_x
+                        pdf_y2 = clip_box.y0 + box[3] * scale_y
+
+                        table_regions.append(((pdf_x1, pdf_y1, pdf_x2, pdf_y2), score))
+
+        except Exception as e:
+            logger.warning(f"  第 {page_num} 页: 版面分析失败 ({e})")
+
+        return table_regions
+
+    def _ocr_table_region(self, page: fitz.Page, bbox: Tuple[float, float, float, float]) -> str:
+        """对指定区域进行 OCR 识别（使用 GLM-OCR）"""
+        # 渲染指定区域
+        rect = fitz.Rect(bbox)
+        pix = page.get_pixmap(dpi=self.OCR_DPI, clip=rect)
+        img_bytes = pix.tobytes("jpeg")
+
+        # 压缩图片
+        compressed = self._compress_image(img_bytes)
+        img_base64 = base64.b64encode(compressed).decode('utf-8')
+
+        # 请求 OCR
+        payload = {
+            "model": "GLM-OCR",
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": "识别图片中的表格内容，按原文排版输出。"
+                                    "注意："
+                                    "1. 表格用 Markdown 表格格式"
+                                    "2. 保持换行和列对齐"
+                                    "3. 只输出表格内容，不要其他说明"
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": f"data:image/jpeg;base64,{img_base64}"}
+                        }
+                    ]
+                }
+            ],
+            "max_tokens": 2048,
+            "temperature": 0.1
+        }
+
+        headers = {"Content-Type": "application/json"}
+        response = requests.post(
+            self.ocr_api_url,
+            headers=headers,
+            json=payload,
+            timeout=self.ocr_timeout
+        )
+        response.raise_for_status()
+
+        result = response.json()
+        return self._extract_ocr_content(result)
+
+    def _replace_table_regions(
+        self,
+        page: fitz.Page,
+        original_text: str,
+        ocr_results: List[Dict],
+        clip_box: fitz.Rect
+    ) -> str:
+        """用 OCR 结果替换原始文本中的表格区域"""
+        if not ocr_results:
+            return original_text
+
+        # 获取页面上的文本块及其坐标
+        text_blocks = []
+        for block in page.get_text("blocks"):
+            x0, y0, x1, y1, text, _, _ = block
+            # 只考虑裁剪区域内的文本
+            if y0 >= clip_box.y0 and y1 <= clip_box.y1:
+                text_blocks.append({
+                    "bbox": (x0, y0, x1, y1),
+                    "text": text.strip(),
+                })
+
+        # 按 Y 坐标排序
+        text_blocks.sort(key=lambda b: (b["bbox"][1], b["bbox"][0]))
+
+        # 找出属于表格区域的文本块
+        replaced_indices: Set[int] = set()
+        for ocr_result in ocr_results:
+            bbox = ocr_result["bbox"]
+            rx0, ry0, rx1, ry1 = bbox
+
+            for idx, block in enumerate(text_blocks):
+                if idx in replaced_indices:
+                    continue
+                bx0, by0, bx1, by1 = block["bbox"]
+
+                # 检查重叠
+                overlap_x = max(0, min(bx1, rx1) - max(bx0, rx0))
+                overlap_y = max(0, min(by1, ry1) - max(by0, ry0))
+                overlap_area = overlap_x * overlap_y
+                block_area = (bx1 - bx0) * (by1 - by0)
+
+                if block_area > 0 and overlap_area / block_area > 0.5:
+                    replaced_indices.add(idx)
+
+        # 构建新文本
+        result_parts: List[str] = []
+        last_idx = 0
+
+        for ocr_result in sorted(ocr_results, key=lambda r: r["bbox"][1]):
+            bbox = ocr_result["bbox"]
+            rx0, ry0, rx1, ry1 = bbox
+
+            # 找到该表格区域之前的文本
+            region_start_idx = None
+            for idx, block in enumerate(text_blocks):
+                if idx in replaced_indices:
+                    bx0, by0, bx1, by1 = block["bbox"]
+                    if (bx0 >= rx0 - 5 and bx1 <= rx1 + 5 and
+                        by0 >= ry0 - 5 and by1 <= ry1 + 5):
+                        if region_start_idx is None:
+                            region_start_idx = idx
+                        last_idx = idx + 1
+
+            if region_start_idx is not None:
+                # 添加表格前的非表格文本
+                for idx in range(last_idx - (last_idx - region_start_idx), region_start_idx):
+                    if idx not in replaced_indices and idx < len(text_blocks):
+                        result_parts.append(text_blocks[idx]["text"])
+                        result_parts.append("\n")
+
+                # 添加 OCR 结果
+                result_parts.append(ocr_result["ocr_text"])
+                result_parts.append("\n")
+
+        # 添加剩余文本
+        for idx in range(last_idx, len(text_blocks)):
+            if idx not in replaced_indices:
+                result_parts.append(text_blocks[idx]["text"])
+                result_parts.append("\n")
+
+        return "".join(result_parts).strip() or original_text
+
+    def _compress_image(self, img_bytes: bytes) -> bytes:
+        """压缩图片"""
+        try:
+            from PIL import Image
+            img = Image.open(io.BytesIO(img_bytes))
+
+            if img.mode in ('RGBA', 'LA', 'P'):
+                background = Image.new('RGB', img.size, (255, 255, 255))
+                if img.mode == 'P':
+                    img = img.convert('RGBA')
+                if img.mode in ('RGBA', 'LA'):
+                    background.paste(img, mask=img.split()[-1])
+                img = background
+            elif img.mode != 'RGB':
+                img = img.convert('RGB')
+
+            min_edge = min(img.size)
+            if min_edge > self.MAX_SHORT_EDGE:
+                ratio = self.MAX_SHORT_EDGE / min_edge
+                new_size = (int(img.width * ratio), int(img.height * ratio))
+                img = img.resize(new_size, Image.Resampling.LANCZOS)
+
+            buffer = io.BytesIO()
+            img.save(buffer, format='JPEG', quality=self.JPEG_QUALITY, optimize=True)
+            return buffer.getvalue()
+
+        except Exception as e:
+            logger.warning(f"图片压缩失败，使用原图: {e}")
+            return img_bytes
+
+    def _extract_ocr_content(self, result: Dict) -> str:
+        """从 OCR 响应提取内容"""
+        if "choices" in result and isinstance(result["choices"], list):
+            if len(result["choices"]) > 0:
+                message = result["choices"][0].get("message", {})
+                return message.get("content", "")
+        return ""
+
     @staticmethod
     def _is_header_footer(line: str) -> bool:
         return (

core/construction_review/component/minimal_pipeline/simple_processor.py

@@ -35,8 +35,8 @@ from ..doc_worker.models import (
 class SimpleDocumentProcessor:
     """最简文档处理器"""
 
-    def __init__(self):
-        self.pdf_extractor = PdfStructureExtractor()
+    def __init__(self, use_ocr: bool = False):
+        self.pdf_extractor = PdfStructureExtractor(use_ocr=use_ocr)
         self.hierarchy_classifier = HierarchyClassifier()
         self.chunk_classifier = ChunkClassifier()