Refactor: migrate pdf_parser.py to golang (#16323)

### What problem does this PR solve?

Http API based on onnx model.
pdf_parser.py to golang

### Type of change

- [x] Refactoring

deepdoc/server/adapters/dla_adapter.py

@@ -0,0 +1,80 @@
+"""DLA adapter — wraps LayoutRecognizer and converts output to wire format."""
+
+import io
+import logging
+from typing import List
+
+from PIL import Image
+
+from deepdoc.vision import LayoutRecognizer
+
+logger = logging.getLogger(__name__)
+
+# OSS model label → Go dlaClassLabels index
+# Go-side (internal/parser/deepdoc.go):
+#   var dlaClassLabels = []string{
+#       "title", "text", "reference", "figure", "figure caption",
+#       "table", "table caption", "table caption", "equation", "figure caption",
+#   }
+# Indices 4/6/7/9 are duplicates; OSS model only produces unique labels.
+DLA_CLASS_MAP = {
+    "title": 0,
+    "text": 1,
+    "reference": 2,
+    "figure": 3,
+    "figure caption": 4,
+    "table": 5,
+    "table caption": 6,
+    "equation": 8,
+}
+
+
+class DLAAdapter:
+    """Calls LayoutRecognizer.forward() and converts bboxes to wire format."""
+
+    def __init__(self, model_dir: str, thr: float = 0.2):
+        self.model_dir = model_dir
+        self.thr = thr
+        self._layouter: LayoutRecognizer | None = None
+
+    def load(self):
+        """Initialize the layout recognizer. Called once per worker."""
+        self._layouter = LayoutRecognizer("layout")
+
+    def __call__(self, image_data: bytes) -> List[List[float]]:
+        """
+        Args:
+            image_data: JPEG image bytes.
+
+        Returns:
+            List of [x0, y0, x1, y1, score, class_id] for each detected layout region.
+        """
+        if self._layouter is None:
+            raise RuntimeError("DLAAdapter.load() must be called before inference")
+
+        img = Image.open(io.BytesIO(image_data)).convert("RGB")
+        width, height = img.size
+
+        # forward() returns raw Recognizer output (no OCR integration)
+        raw_bboxes = self._layouter.forward([img], thr=self.thr, batch_size=1)[0]
+
+        result = []
+        for b in raw_bboxes:
+            label = b["type"].lower()
+            class_id = DLA_CLASS_MAP.get(label)
+            if class_id is None:
+                logger.warning("DLA: unknown label '%s', skipping", label)
+                continue
+
+            x0, y0, x1, y1 = b["bbox"]
+            score = float(b["score"])
+
+            # Clamp coordinates
+            x0 = max(0.0, min(float(x0), width))
+            y0 = max(0.0, min(float(y0), height))
+            x1 = max(0.0, min(float(x1), width))
+            y1 = max(0.0, min(float(y1), height))
+
+            result.append([x0, y0, x1, y1, score, float(class_id)])
+
+        return result