fix: encode non-ASCII filename in Content-Disposition header

Use RFC 5987 filename*=UTF-8'' percent-encoding to support Chinese and
other Unicode characters in DOCX download filenames.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.claude/settings.local.json

@@ -8,7 +8,8 @@
       "WebFetch(domain:raw.githubusercontent.com)",
       "Bash(python -c \"\nfrom app.services.glm_postprocess import GLMResultFormatter, clean_repeated_content, clean_formula_number\nf = GLMResultFormatter\\(\\)\nprint\\('GLMResultFormatter OK'\\)\nprint\\('clean_formula_number:', clean_formula_number\\('\\(2.1\\)'\\)\\)\nregions = [\n    {'index': 0, 'label': 'text', 'native_label': 'doc_title', 'content': 'Introduction', 'bbox_2d': [10,10,990,50]},\n    {'index': 1, 'label': 'formula', 'native_label': 'display_formula', 'content': r'\\\\frac{a}{b}', 'bbox_2d': [10,60,990,200]},\n    {'index': 2, 'label': 'text', 'native_label': 'formula_number', 'content': '\\(1\\)', 'bbox_2d': [900,60,990,200]},\n]\nmd = f.process\\(regions\\)\nprint\\('process output:'\\)\nprint\\(md\\)\n\" 2>&1 | grep -v \"^$\")",
       "Bash(python3 -c \"\nfrom app.services.glm_postprocess import GLMResultFormatter, clean_repeated_content, clean_formula_number\nf = GLMResultFormatter\\(\\)\nprint\\('GLMResultFormatter OK'\\)\nprint\\('clean_formula_number:', clean_formula_number\\('\\(2.1\\)'\\)\\)\nregions = [\n    {'index': 0, 'label': 'text', 'native_label': 'doc_title', 'content': 'Introduction', 'bbox_2d': [10,10,990,50]},\n    {'index': 1, 'label': 'formula', 'native_label': 'display_formula', 'content': r'\\\\frac{a}{b}', 'bbox_2d': [10,60,990,200]},\n    {'index': 2, 'label': 'text', 'native_label': 'formula_number', 'content': '\\(1\\)', 'bbox_2d': [900,60,990,200]},\n]\nmd = f.process\\(regions\\)\nprint\\('process output:'\\)\nprint\\(repr\\(md\\)\\)\n\" 2>&1)",
-      "Bash(ls .venv 2>/dev/null || ls venv 2>/dev/null || echo \"no venv found\" && find . -name \"activate\" -path \"*/bin/activate\" 2>/dev/null | head -3)"
+      "Bash(ls .venv 2>/dev/null || ls venv 2>/dev/null || echo \"no venv found\" && find . -name \"activate\" -path \"*/bin/activate\" 2>/dev/null | head -3)",
+      "Bash(ruff check:*)"
     ]
   }
 }

app/api/v1/endpoints/convert.py

@@ -1,12 +1,17 @@
 """Format conversion endpoints."""
 
+from urllib.parse import quote
+
 from fastapi import APIRouter, Depends, HTTPException
 from fastapi.responses import Response
 
 from app.core.dependencies import get_converter
-from app.schemas.convert import MarkdownToDocxRequest, LatexToOmmlRequest, LatexToOmmlResponse
+from app.core.logging_config import get_logger
+from app.schemas.convert import LatexToOmmlRequest, LatexToOmmlResponse, MarkdownToDocxRequest
 from app.services.converter import Converter
 
+logger = get_logger()
+
 router = APIRouter()
 
 
@@ -19,14 +24,26 @@ async def convert_markdown_to_docx(
 
     Returns the generated DOCX file as a binary response.
     """
+    logger.info(
+        "Converting markdown to DOCX, filename=%s, content_length=%d",
+        request.filename,
+        len(request.markdown),
+    )
     try:
         docx_bytes = converter.export_to_file(request.markdown, export_type="docx")
+        logger.info(
+            "DOCX conversion successful, filename=%s, size=%d bytes",
+            request.filename,
+            len(docx_bytes),
+        )
+        encoded_name = quote(f"{request.filename}.docx")
         return Response(
             content=docx_bytes,
             media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-            headers={"Content-Disposition": f'attachment; filename="{request.filename}.docx"'},
+            headers={"Content-Disposition": f"attachment; filename*=UTF-8''{encoded_name}"},
         )
     except Exception as e:
+        logger.exception("DOCX conversion failed, filename=%s: %s", request.filename, e)
         raise HTTPException(status_code=500, detail=f"Conversion failed: {e}")
 
 
@@ -55,12 +72,17 @@ async def convert_latex_to_omml(
         ```
     """
     if not request.latex or not request.latex.strip():
+        logger.warning("LaTeX to OMML request received with empty formula")
         raise HTTPException(status_code=400, detail="LaTeX formula cannot be empty")
 
+    logger.info("Converting LaTeX to OMML, latex=%r", request.latex)
     try:
         omml = converter.convert_to_omml(request.latex)
+        logger.info("LaTeX to OMML conversion successful")
         return LatexToOmmlResponse(omml=omml)
     except ValueError as e:
+        logger.warning("LaTeX to OMML conversion invalid input: %s", e)
         raise HTTPException(status_code=400, detail=str(e))
     except RuntimeError as e:
+        logger.error("LaTeX to OMML conversion runtime error: %s", e)
         raise HTTPException(status_code=503, detail=str(e))

app/api/v1/endpoints/image.py

@@ -6,10 +6,10 @@ import uuid
 from fastapi import APIRouter, Depends, HTTPException, Request, Response
 
 from app.core.dependencies import (
-    get_image_processor,
     get_glmocr_endtoend_service,
+    get_image_processor,
 )
-from app.core.logging_config import get_logger, RequestIDAdapter
+from app.core.logging_config import RequestIDAdapter, get_logger
 from app.schemas.image import ImageOCRRequest, ImageOCRResponse
 from app.services.image_processor import ImageProcessor
 from app.services.ocr_service import GLMOCREndToEndService

app/core/config.py

@@ -50,9 +50,7 @@ class Settings(BaseSettings):
     max_tokens: int = 4096
 
     # Model Paths
-    pp_doclayout_model_dir: str | None = (
+    pp_doclayout_model_dir: str | None = "/home/yoge/.cache/modelscope/hub/models/PaddlePaddle/PP-DocLayoutV3"
-        "/home/yoge/.cache/modelscope/hub/models/PaddlePaddle/PP-DocLayoutV3"
-    )
 
     # Image Processing
     max_image_size_mb: int = 10

app/core/dependencies.py

@@ -1,10 +1,10 @@
 """Application dependencies."""
 
+from app.core.config import get_settings
+from app.services.converter import Converter
 from app.services.image_processor import ImageProcessor
 from app.services.layout_detector import LayoutDetector
 from app.services.ocr_service import GLMOCREndToEndService
-from app.services.converter import Converter
-from app.core.config import get_settings
 
 # Global instances (initialized on startup)
 _layout_detector: LayoutDetector | None = None

app/core/logging_config.py

@@ -2,11 +2,15 @@
 
 import logging
 import logging.handlers
+from contextvars import ContextVar
 from pathlib import Path
-from typing import Any, Optional
+from typing import Any
 
 from app.core.config import get_settings
 
+# Context variable to hold the current request_id across async boundaries
+request_id_ctx: ContextVar[str] = ContextVar("request_id", default="-")
+
 
 class TimedRotatingAndSizeFileHandler(logging.handlers.TimedRotatingFileHandler):
     """File handler that rotates by both time (daily) and size (100MB)."""
@@ -18,10 +22,10 @@ class TimedRotatingAndSizeFileHandler(logging.handlers.TimedRotatingFileHandler)
         interval: int = 1,
         backupCount: int = 30,
         maxBytes: int = 100 * 1024 * 1024,  # 100MB
-        encoding: Optional[str] = None,
+        encoding: str | None = None,
         delay: bool = False,
         utc: bool = False,
-        atTime: Optional[Any] = None,
+        atTime: Any | None = None,
     ):
         """Initialize handler with both time and size rotation.
 
@@ -58,14 +62,14 @@ class TimedRotatingAndSizeFileHandler(logging.handlers.TimedRotatingFileHandler)
         if self.stream is None:
             self.stream = self._open()
         if self.maxBytes > 0:
-            msg = "%s\n" % self.format(record)
+            msg = f"{self.format(record)}\n"
             self.stream.seek(0, 2)  # Seek to end
             if self.stream.tell() + len(msg) >= self.maxBytes:
                 return True
         return False
 
 
-def setup_logging(log_dir: Optional[str] = None) -> logging.Logger:
+def setup_logging(log_dir: str | None = None) -> logging.Logger:
     """Setup application logging with rotation by day and size.
 
     Args:
@@ -92,14 +96,13 @@ def setup_logging(log_dir: Optional[str] = None) -> logging.Logger:
     # Remove existing handlers to avoid duplicates
     logger.handlers.clear()
 
-    # Create custom formatter that handles missing request_id
+    # Create custom formatter that automatically injects request_id from context
     class RequestIDFormatter(logging.Formatter):
-        """Formatter that handles request_id in log records."""
+        """Formatter that injects request_id from ContextVar into log records."""
 
         def format(self, record):
-            # Add request_id if not present
             if not hasattr(record, "request_id"):
-                record.request_id = getattr(record, "request_id", "unknown")
+                record.request_id = request_id_ctx.get()
             return super().format(record)
 
     formatter = RequestIDFormatter(
@@ -134,11 +137,11 @@ def setup_logging(log_dir: Optional[str] = None) -> logging.Logger:
 
 
 # Global logger instance
-_logger: Optional[logging.Logger] = None
+_logger: logging.Logger | None = None
 
 
 def get_logger() -> logging.Logger:
-    """Get the global logger instance."""
+    """Get the global logger instance, initializing if needed."""
     global _logger
     if _logger is None:
         _logger = setup_logging()

app/main.py

@@ -8,6 +8,7 @@ from app.api.v1.router import api_router
 from app.core.config import get_settings
 from app.core.dependencies import init_layout_detector
 from app.core.logging_config import setup_logging
+from app.middleware.request_id import RequestIDMiddleware
 
 settings = get_settings()
 
@@ -33,6 +34,8 @@ app = FastAPI(
     lifespan=lifespan,
 )
 
+app.add_middleware(RequestIDMiddleware)
+
 # Include API router
 app.include_router(api_router, prefix=settings.api_prefix)
 

app/middleware/request_id.py

@@ -0,0 +1,34 @@
+"""Middleware to propagate or generate request_id for every request."""
+
+import uuid
+
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.requests import Request
+from starlette.responses import Response
+
+from app.core.logging_config import request_id_ctx
+
+REQUEST_ID_HEADER = "X-Request-ID"
+
+
+class RequestIDMiddleware(BaseHTTPMiddleware):
+    """Extract X-Request-ID from incoming request headers or generate one.
+
+    The request_id is stored in a ContextVar so that all log records emitted
+    during the request are automatically annotated with it, without needing to
+    pass it explicitly through every call.
+
+    The same request_id is also echoed back in the response header so that
+    callers can correlate logs.
+    """
+
+    async def dispatch(self, request: Request, call_next) -> Response:
+        request_id = request.headers.get(REQUEST_ID_HEADER) or str(uuid.uuid4())
+        token = request_id_ctx.set(request_id)
+        try:
+            response = await call_next(request)
+        finally:
+            request_id_ctx.reset(token)
+
+        response.headers[REQUEST_ID_HEADER] = request_id
+        return response

app/schemas/convert.py

@@ -36,4 +36,3 @@ class LatexToOmmlResponse(BaseModel):
     """Response body for LaTeX to OMML conversion endpoint."""
 
     omml: str = Field("", description="OMML (Office Math Markup Language) representation")
-

app/schemas/image.py

@@ -7,7 +7,9 @@ class LayoutRegion(BaseModel):
     """A detected layout region in the document."""
 
     type: str = Field(..., description="Region type: text, formula, table, figure")
-    native_label: str = Field("", description="Raw label before type mapping (e.g. doc_title, formula_number)")
+    native_label: str = Field(
+        "", description="Raw label before type mapping (e.g. doc_title, formula_number)"
+    )
     bbox: list[float] = Field(..., description="Bounding box [x1, y1, x2, y2]")
     confidence: float = Field(..., description="Detection confidence score")
     score: float = Field(..., description="Detection score")
@@ -41,10 +43,15 @@ class ImageOCRRequest(BaseModel):
 class ImageOCRResponse(BaseModel):
     """Response body for image OCR endpoint."""
 
-    latex: str = Field("", description="LaTeX representation of the content (empty if mixed content)")
+    latex: str = Field(
+        "", description="LaTeX representation of the content (empty if mixed content)"
+    )
     markdown: str = Field("", description="Markdown representation of the content")
     mathml: str = Field("", description="Standard MathML representation (empty if mixed content)")
-    mml: str = Field("", description="XML MathML with mml: namespace prefix (empty if mixed content)")
+    mml: str = Field(
+        "", description="XML MathML with mml: namespace prefix (empty if mixed content)"
+    )
     layout_info: LayoutInfo = Field(default_factory=LayoutInfo)
-    recognition_mode: str = Field("", description="Recognition mode used: mixed_recognition or formula_recognition")
+    recognition_mode: str = Field(
-
+        "", description="Recognition mode used: mixed_recognition or formula_recognition"
+    )

app/services/converter.py

@@ -112,14 +112,18 @@ class Converter:
     # Pre-compiled regex patterns for preprocessing
     _RE_VSPACE = re.compile(r"\\\[1mm\]")
     _RE_BLOCK_FORMULA_INLINE = re.compile(r"([^\n])(\s*)\\\[(.*?)\\\]([^\n])", re.DOTALL)
-    _RE_BLOCK_FORMULA_LINE = re.compile(r"^(\s*)\\\[(.*?)\\\](\s*)(?=\n|$)", re.MULTILINE | re.DOTALL)
+    _RE_BLOCK_FORMULA_LINE = re.compile(
+        r"^(\s*)\\\[(.*?)\\\](\s*)(?=\n|$)", re.MULTILINE | re.DOTALL
+    )
     _RE_ARITHMATEX = re.compile(r'<span class="arithmatex">(.*?)</span>')
     _RE_INLINE_SPACE = re.compile(r"(?<!\$)\$ +(.+?) +\$(?!\$)")
     _RE_ARRAY_SPECIFIER = re.compile(r"\\begin\{array\}\{([^}]+)\}")
     _RE_LEFT_BRACE = re.compile(r"\\left\\\{\s+")
     _RE_RIGHT_BRACE = re.compile(r"\s+\\right\\\}")
     _RE_CASES = re.compile(r"\\begin\{cases\}(.*?)\\end\{cases\}", re.DOTALL)
-    _RE_ALIGNED_BRACE = re.compile(r"\\left\\\{\\begin\{aligned\}(.*?)\\end\{aligned\}\\right\.", re.DOTALL)
+    _RE_ALIGNED_BRACE = re.compile(
+        r"\\left\\\{\\begin\{aligned\}(.*?)\\end\{aligned\}\\right\.", re.DOTALL
+    )
     _RE_ALIGNED = re.compile(r"\\begin\{aligned\}(.*?)\\end\{aligned\}", re.DOTALL)
     _RE_TAG = re.compile(r"\$\$(.*?)\\tag\s*\{([^}]+)\}\s*\$\$", re.DOTALL)
     _RE_VMATRIX = re.compile(r"\\begin\{vmatrix\}(.*?)\\end\{vmatrix\}", re.DOTALL)
@@ -368,7 +372,9 @@ class Converter:
                 mathml = latex_to_mathml(latex_formula)
                 return Converter._postprocess_mathml_for_word(mathml)
             except Exception as e:
-                raise RuntimeError(f"MathML conversion failed: {pandoc_error}. latex2mathml fallback also failed: {e}") from e
+                raise RuntimeError(
+                    f"MathML conversion failed: {pandoc_error}. latex2mathml fallback also failed: {e}"
+                ) from e
 
     @staticmethod
     def _postprocess_mathml_for_word(mathml: str) -> str:
@@ -583,7 +589,6 @@ class Converter:
             "&#x21D3;": "⇓",  # Downarrow
             "&#x2195;": "↕",  # updownarrow
             "&#x21D5;": "⇕",  # Updownarrow
-            "&#x2260;": "≠",  # ne
             "&#x226A;": "≪",  # ll
             "&#x226B;": "≫",  # gg
             "&#x2A7D;": "⩽",  # leqslant
@@ -962,7 +967,7 @@ class Converter:
         """Export to DOCX format using pypandoc."""
         extra_args = [
             "--highlight-style=pygments",
-            f"--reference-doc=app/pkg/reference.docx",
+            "--reference-doc=app/pkg/reference.docx",
         ]
         pypandoc.convert_file(
             input_path,

app/services/glm_postprocess.py

@@ -1,26 +1,10 @@
-"""GLM-OCR postprocessing logic adapted for this project.
-
-Ported from glm-ocr/glmocr/postprocess/result_formatter.py and
-glm-ocr/glmocr/utils/result_postprocess_utils.py.
-
-Covers:
-- Repeated-content / hallucination detection
-- Per-region content cleaning and formatting (titles, bullets, formulas)
-- formula_number merging (→ \\tag{})
-- Hyphenated text-block merging (via wordfreq)
-- Missing bullet-point detection
-"""
-
 from __future__ import annotations
 
 import logging
 import re
-import json
-
-logger = logging.getLogger(__name__)
 from collections import Counter
 from copy import deepcopy
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any
 
 try:
     from wordfreq import zipf_frequency
@@ -29,13 +13,14 @@ try:
 except ImportError:
     _WORDFREQ_AVAILABLE = False
 
+logger = logging.getLogger(__name__)
 
 # ---------------------------------------------------------------------------
 # result_postprocess_utils (ported)
 # ---------------------------------------------------------------------------
 
 
-def find_consecutive_repeat(s: str, min_unit_len: int = 10, min_repeats: int = 10) -> Optional[str]:
+def find_consecutive_repeat(s: str, min_unit_len: int = 10, min_repeats: int = 10) -> str | None:
     """Detect and truncate a consecutively-repeated pattern.
 
     Returns the string with the repeat removed, or None if not found.
@@ -106,7 +91,7 @@ def clean_formula_number(number_content: str) -> str:
     # Strip display math delimiters
     for start, end in [("$$", "$$"), (r"\[", r"\]"), ("$", "$"), (r"\(", r"\)")]:
         if s.startswith(start) and s.endswith(end) and len(s) > len(start) + len(end):
-            s = s[len(start):-len(end)].strip()
+            s = s[len(start) : -len(end)].strip()
             break
     # Strip CJK/ASCII parentheses
     if s.startswith("(") and s.endswith(")"):
@@ -120,8 +105,13 @@ def clean_formula_number(number_content: str) -> str:
 # GLMResultFormatter
 # ---------------------------------------------------------------------------
 
+# Matches content that consists *entirely* of a display-math block and nothing else.
+# Used to detect when a text/heading region was actually recognised as a formula by vLLM,
+# so we can correct the label before heading prefixes (## …) are applied.
+_PURE_DISPLAY_FORMULA_RE = re.compile(r"^\s*(?:\$\$[\s\S]+?\$\$|\\\[[\s\S]+?\\\])\s*$")
+
 # Label → canonical category mapping (mirrors GLM-OCR label_visualization_mapping)
-_LABEL_TO_CATEGORY: Dict[str, str] = {
+_LABEL_TO_CATEGORY: dict[str, str] = {
     # text
     "abstract": "text",
     "algorithm": "text",
@@ -157,7 +147,7 @@ class GLMResultFormatter:
     # Public entry-point
     # ------------------------------------------------------------------ #
 
-    def process(self, regions: List[Dict[str, Any]]) -> str:
+    def process(self, regions: list[dict[str, Any]]) -> str:
         """Run the full postprocessing pipeline and return Markdown.
 
         Args:
@@ -175,11 +165,24 @@ class GLMResultFormatter:
         items = sorted(deepcopy(regions), key=lambda x: x.get("index", 0))
 
         # Per-region cleaning + formatting
-        processed: List[Dict] = []
+        processed: list[dict] = []
         for item in items:
             item["native_label"] = item.get("native_label", item.get("label", "text"))
             item["label"] = self._map_label(item.get("label", "text"), item["native_label"])
 
+            # Label correction: layout may say "text" (or a heading like "paragraph_title")
+            # but vLLM recognised the content as a formula and returned $$…$$.  Without
+            # correction the heading prefix (##) would be prepended to the math block,
+            # producing broken output like "## $$ \mathbf{y}=… $$".
+            raw_content = (item.get("content") or "").strip()
+            if item["label"] == "text" and _PURE_DISPLAY_FORMULA_RE.match(raw_content):
+                logger.debug(
+                    "Label corrected text (native=%s) → formula: pure display-formula detected",
+                    item["native_label"],
+                )
+                item["label"] = "formula"
+                item["native_label"] = "display_formula"
+
             item["content"] = self._format_content(
                 item.get("content") or "",
                 item["label"],
@@ -199,7 +202,7 @@ class GLMResultFormatter:
         processed = self._format_bullet_points(processed)
 
         # Assemble Markdown
-        parts: List[str] = []
+        parts: list[str] = []
         for item in processed:
             content = item.get("content") or ""
             if item["label"] == "image":
@@ -262,9 +265,11 @@ class GLMResultFormatter:
         # Formula wrapping
         if label == "formula":
             content = content.strip()
-            for s, e in [("$$", "$$"), (r"\[", r"\]"), (r"\(", r"\)")]:
+            for s, e in [("$$", "$$"), (r"\[", r"\]"), (r"\(", r"\)"), ("$", "$")]:
-                if content.startswith(s) and content.endswith(e):
+                if content.startswith(s):
-                    content = content[len(s) : -len(e)].strip()
+                    content = content[len(s) :].strip()
+                    if content.endswith(e):
+                        content = content[: -len(e)].strip()
                     break
             if not content:
                 logger.warning("Skipping formula region with empty content after stripping delimiters")
@@ -296,12 +301,12 @@ class GLMResultFormatter:
     # Structural merges
     # ------------------------------------------------------------------ #
 
-    def _merge_formula_numbers(self, items: List[Dict]) -> List[Dict]:
+    def _merge_formula_numbers(self, items: list[dict]) -> list[dict]:
         """Merge formula_number region into adjacent formula with \\tag{}."""
         if not items:
             return items
 
-        merged: List[Dict] = []
+        merged: list[dict] = []
         skip: set = set()
 
         for i, block in enumerate(items):
@@ -340,12 +345,12 @@ class GLMResultFormatter:
             block["index"] = i
         return merged
 
-    def _merge_text_blocks(self, items: List[Dict]) -> List[Dict]:
+    def _merge_text_blocks(self, items: list[dict]) -> list[dict]:
         """Merge hyphenated text blocks when the combined word is valid (wordfreq)."""
         if not items or not _WORDFREQ_AVAILABLE:
             return items
 
-        merged: List[Dict] = []
+        merged: list[dict] = []
         skip: set = set()
 
         for i, block in enumerate(items):
@@ -389,7 +394,7 @@ class GLMResultFormatter:
             block["index"] = i
         return merged
 
-    def _format_bullet_points(self, items: List[Dict], left_align_threshold: float = 10.0) -> List[Dict]:
+    def _format_bullet_points(self, items: list[dict], left_align_threshold: float = 10.0) -> list[dict]:
         """Add missing bullet prefix when a text block is sandwiched between two bullet items."""
         if len(items) < 3:
             return items
@@ -419,10 +424,7 @@ class GLMResultFormatter:
             if not (cur_bbox and prev_bbox and nxt_bbox):
                 continue
 
-            if (
+            if abs(cur_bbox[0] - prev_bbox[0]) <= left_align_threshold and abs(cur_bbox[0] - nxt_bbox[0]) <= left_align_threshold:
-                abs(cur_bbox[0] - prev_bbox[0]) <= left_align_threshold
-                and abs(cur_bbox[0] - nxt_bbox[0]) <= left_align_threshold
-            ):
                 cur["content"] = "- " + cur_content
 
         return items

app/services/layout_detector.py

@@ -1,12 +1,11 @@
 """PP-DocLayoutV3 wrapper for document layout detection."""
 
 import numpy as np
-
-from app.schemas.image import LayoutInfo, LayoutRegion
-from app.core.config import get_settings
-from app.services.layout_postprocess import apply_layout_postprocess
 from paddleocr import LayoutDetection
-from typing import Optional
+
+from app.core.config import get_settings
+from app.schemas.image import LayoutInfo, LayoutRegion
+from app.services.layout_postprocess import apply_layout_postprocess
 
 settings = get_settings()
 
@@ -14,7 +13,7 @@ settings = get_settings()
 class LayoutDetector:
     """Layout detector for PP-DocLayoutV2."""
 
-    _layout_detector: Optional[LayoutDetection] = None
+    _layout_detector: LayoutDetection | None = None
 
     # PP-DocLayoutV2 class ID to label mapping
     CLS_ID_TO_LABEL: dict[int, str] = {
@@ -149,17 +148,18 @@ class LayoutDetector:
                 )
             )
 
-        mixed_recognition = any(region.type == "text" and region.score > 0.3 for region in regions)
+        mixed_recognition = any(region.type == "text" and region.score > 0.85 for region in regions)
 
         return LayoutInfo(regions=regions, MixedRecognition=mixed_recognition)
 
 
 if __name__ == "__main__":
     import cv2
+
     from app.core.config import get_settings
-    from app.services.image_processor import ImageProcessor
     from app.services.converter import Converter
-    from app.services.ocr_service import OCRService
+    from app.services.image_processor import ImageProcessor
+    from app.services.ocr_service import GLMOCREndToEndService
 
     settings = get_settings()
 
@@ -169,15 +169,15 @@ if __name__ == "__main__":
     converter = Converter()
 
     # Initialize OCR service
-    ocr_service = OCRService(
+    ocr_service = GLMOCREndToEndService(
-        vl_server_url=settings.paddleocr_vl_url,
+        vl_server_url=settings.glm_ocr_url,
         layout_detector=layout_detector,
         image_processor=image_processor,
         converter=converter,
     )
 
     # Load test image
-    image_path = "test/timeout.jpg"
+    image_path = "test/image2.png"
     image = cv2.imread(image_path)
 
     if image is None:

app/services/layout_postprocess.py

@@ -15,16 +15,14 @@ the quality of the GLM-OCR SDK's layout pipeline.
 
 from __future__ import annotations
 
-from typing import Dict, List, Optional, Tuple, Union
-
 import numpy as np
 
-
 # ---------------------------------------------------------------------------
 # Primitive geometry helpers
 # ---------------------------------------------------------------------------
 
-def iou(box1: List[float], box2: List[float]) -> float:
+
+def iou(box1: list[float], box2: list[float]) -> float:
     """Compute IoU of two bounding boxes [x1, y1, x2, y2]."""
     x1, y1, x2, y2 = box1
     x1_p, y1_p, x2_p, y2_p = box2
@@ -41,7 +39,7 @@ def iou(box1: List[float], box2: List[float]) -> float:
     return inter_area / float(box1_area + box2_area - inter_area)
 
 
-def is_contained(box1: List[float], box2: List[float], overlap_threshold: float = 0.8) -> bool:
+def is_contained(box1: list[float], box2: list[float], overlap_threshold: float = 0.8) -> bool:
     """Return True if box1 is contained within box2 (overlap ratio >= threshold).
 
     box format: [cls_id, score, x1, y1, x2, y2]
@@ -66,11 +64,12 @@ def is_contained(box1: List[float], box2: List[float], overlap_threshold: float
 # NMS
 # ---------------------------------------------------------------------------
 
+
 def nms(
     boxes: np.ndarray,
     iou_same: float = 0.6,
     iou_diff: float = 0.98,
-) -> List[int]:
+) -> list[int]:
     """NMS with separate IoU thresholds for same-class and cross-class overlaps.
 
     Args:
@@ -83,7 +82,7 @@ def nms(
     """
     scores = boxes[:, 1]
     indices = np.argsort(scores)[::-1].tolist()
-    selected: List[int] = []
+    selected: list[int] = []
 
     while indices:
         current = indices[0]
@@ -114,10 +113,10 @@ _PRESERVE_LABELS = {"image", "seal", "chart"}
 
 def check_containment(
     boxes: np.ndarray,
-    preserve_cls_ids: Optional[set] = None,
+    preserve_cls_ids: set | None = None,
-    category_index: Optional[int] = None,
+    category_index: int | None = None,
-    mode: Optional[str] = None,
+    mode: str | None = None,
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     """Compute containment flags for each box.
 
     Args:
@@ -160,9 +159,10 @@ def check_containment(
 # Box expansion (unclip)
 # ---------------------------------------------------------------------------
 
+
 def unclip_boxes(
     boxes: np.ndarray,
-    unclip_ratio: Union[float, Tuple[float, float], Dict, List, None],
+    unclip_ratio: float | tuple[float, float] | dict | list | None,
 ) -> np.ndarray:
     """Expand bounding boxes by the given ratio.
 
@@ -215,13 +215,14 @@ def unclip_boxes(
 # Main entry-point
 # ---------------------------------------------------------------------------
 
+
 def apply_layout_postprocess(
-    boxes: List[Dict],
+    boxes: list[dict],
-    img_size: Tuple[int, int],
+    img_size: tuple[int, int],
     layout_nms: bool = True,
-    layout_unclip_ratio: Union[float, Tuple, Dict, None] = None,
+    layout_unclip_ratio: float | tuple | dict | None = None,
-    layout_merge_bboxes_mode: Union[str, Dict, None] = "large",
+    layout_merge_bboxes_mode: str | dict | None = "large",
-) -> List[Dict]:
+) -> list[dict]:
     """Apply GLM-OCR layout post-processing to PaddleOCR detection results.
 
     Args:
@@ -250,7 +251,7 @@ def apply_layout_postprocess(
         arr_rows.append([cls_id, score, x1, y1, x2, y2])
     boxes_array = np.array(arr_rows, dtype=float)
 
-    all_labels: List[str] = [b.get("label", "") for b in boxes]
+    all_labels: list[str] = [b.get("label", "") for b in boxes]
 
     # 1. NMS ---------------------------------------------------------------- #
     if layout_nms and len(boxes_array) > 1:
@@ -262,17 +263,14 @@ def apply_layout_postprocess(
     if len(boxes_array) > 1:
         img_area = img_width * img_height
         area_thres = 0.82 if img_width > img_height else 0.93
-        image_cls_ids = {
-            int(boxes_array[i, 0])
-            for i, lbl in enumerate(all_labels)
-            if lbl == "image"
-        }
         keep_mask = np.ones(len(boxes_array), dtype=bool)
         for i, lbl in enumerate(all_labels):
             if lbl == "image":
                 x1, y1, x2, y2 = boxes_array[i, 2:6]
-                x1 = max(0.0, x1); y1 = max(0.0, y1)
+                x1 = max(0.0, x1)
-                x2 = min(float(img_width), x2); y2 = min(float(img_height), y2)
+                y1 = max(0.0, y1)
+                x2 = min(float(img_width), x2)
+                y2 = min(float(img_height), y2)
                 if (x2 - x1) * (y2 - y1) > area_thres * img_area:
                     keep_mask[i] = False
         boxes_array = boxes_array[keep_mask]
@@ -281,9 +279,7 @@ def apply_layout_postprocess(
     # 3. Containment analysis (merge_bboxes_mode) -------------------------- #
     if layout_merge_bboxes_mode and len(boxes_array) > 1:
         preserve_cls_ids = {
-            int(boxes_array[i, 0])
+            int(boxes_array[i, 0]) for i, lbl in enumerate(all_labels) if lbl in _PRESERVE_LABELS
-            for i, lbl in enumerate(all_labels)
-            if lbl in _PRESERVE_LABELS
         }
 
         if isinstance(layout_merge_bboxes_mode, str):
@@ -321,7 +317,7 @@ def apply_layout_postprocess(
         boxes_array = unclip_boxes(boxes_array, layout_unclip_ratio)
 
     # 5. Clamp to image boundaries + skip invalid -------------------------- #
-    result: List[Dict] = []
+    result: list[dict] = []
     for i, row in enumerate(boxes_array):
         cls_id = int(row[0])
         score = float(row[1])
@@ -333,11 +329,13 @@ def apply_layout_postprocess(
         if x1 >= x2 or y1 >= y2:
             continue
 
-        result.append({
+        result.append(
-            "cls_id": cls_id,
+            {
-            "label": all_labels[i],
+                "cls_id": cls_id,
-            "score": score,
+                "label": all_labels[i],
-            "coordinate": [int(x1), int(y1), int(x2), int(y2)],
+                "score": score,
-        })
+                "coordinate": [int(x1), int(y1), int(x2), int(y2)],
+            }
+        )
 
     return result

app/services/ocr_service.py

@@ -150,9 +150,7 @@ def _clean_latex_syntax_spaces(expr: str) -> str:
             # Strategy: remove spaces before \ and between non-command chars,
             # but preserve the space after \command when followed by a non-\ char
             cleaned = re.sub(r"\s+(?=\\)", "", content)  # remove space before \cmd
-            cleaned = re.sub(
+            cleaned = re.sub(r"(?<!\\)(?<![a-zA-Z])\s+", "", cleaned)  # remove space after non-letter non-\
-                r"(?<!\\)(?<![a-zA-Z])\s+", "", cleaned
-            )  # remove space after non-letter non-\
         return f"{operator}{{{cleaned}}}"
 
     # Match _{ ... } or ^{ ... }
@@ -630,9 +628,7 @@ class MineruOCRService(OCRServiceBase):
         self.glm_ocr_url = glm_ocr_url
         self.openai_client = OpenAI(api_key="EMPTY", base_url=glm_ocr_url, timeout=3600)
 
-    def _recognize_formula_with_paddleocr_vl(
+    def _recognize_formula_with_paddleocr_vl(self, image: np.ndarray, prompt: str = "Formula Recognition:") -> str:
-        self, image: np.ndarray, prompt: str = "Formula Recognition:"
-    ) -> str:
         """Recognize formula using PaddleOCR-VL API.
 
         Args:
@@ -673,9 +669,7 @@ class MineruOCRService(OCRServiceBase):
         except Exception as e:
             raise RuntimeError(f"PaddleOCR-VL formula recognition failed: {e}") from e
 
-    def _extract_and_recognize_formulas(
+    def _extract_and_recognize_formulas(self, markdown_content: str, original_image: np.ndarray) -> str:
-        self, markdown_content: str, original_image: np.ndarray
-    ) -> str:
         """Extract image references from markdown and recognize formulas.
 
         Args:
@@ -757,9 +751,7 @@ class MineruOCRService(OCRServiceBase):
                 markdown_content = result["results"]["image"].get("md_content", "")
 
             if "![](images/" in markdown_content:
-                markdown_content = self._extract_and_recognize_formulas(
+                markdown_content = self._extract_and_recognize_formulas(markdown_content, original_image)
-                    markdown_content, original_image
-                )
 
             # Apply postprocessing to fix OCR errors
             markdown_content = _postprocess_markdown(markdown_content)
@@ -789,15 +781,11 @@ class MineruOCRService(OCRServiceBase):
 
 # Task-specific prompts (from GLM-OCR SDK config.yaml)
 _TASK_PROMPTS: dict[str, str] = {
-    "text": "Text Recognition:",
+    "text": "Text Recognition. If the content is a formula, please output display latex code, else output text",
     "formula": "Formula Recognition:",
     "table": "Table Recognition:",
 }
-_DEFAULT_PROMPT = (
+_DEFAULT_PROMPT = "Text Recognition. If the content is a formula, please output display latex code, else output text"
-    "Recognize the text in the image and output in Markdown format. "
-    "Preserve the original layout (headings/paragraphs/tables/formulas). "
-    "Do not fabricate content that does not exist in the image."
-)
 
 
 class GLMOCREndToEndService(OCRServiceBase):
@@ -878,21 +866,19 @@ class GLMOCREndToEndService(OCRServiceBase):
         Returns:
             Dict with 'markdown', 'latex', 'mathml', 'mml' keys.
         """
-        # 1. Padding
+        # 1. Layout detection
-        padded = self.image_processor.add_padding(image)
+        img_h, img_w = image.shape[:2]
-        img_h, img_w = padded.shape[:2]
+        padded_image = self.image_processor.add_padding(image)
-
+        layout_info = self.layout_detector.detect(padded_image)
-        # 2. Layout detection
-        layout_info = self.layout_detector.detect(padded)
 
         # Sort regions in reading order: top-to-bottom, left-to-right
         layout_info.regions.sort(key=lambda r: (r.bbox[1], r.bbox[0]))
 
         # 3. OCR: per-region (parallel) or full-image fallback
-        if not layout_info.regions:
+        if not layout_info.regions or (len(layout_info.regions) == 1 and not layout_info.MixedRecognition):
             # No layout detected → assume it's a formula, use formula recognition
             logger.info("No layout regions detected, treating image as formula")
-            raw_content = self._call_vllm(padded, _TASK_PROMPTS["formula"])
+            raw_content = self._call_vllm(image, _TASK_PROMPTS["formula"])
             # Format as display formula markdown
             formatted_content = raw_content.strip()
             if not (formatted_content.startswith("$$") and formatted_content.endswith("$$")):
@@ -905,7 +891,7 @@ class GLMOCREndToEndService(OCRServiceBase):
                 if region.type == "figure":
                     continue
                 x1, y1, x2, y2 = (int(c) for c in region.bbox)
-                cropped = padded[y1:y2, x1:x2]
+                cropped = padded_image[y1:y2, x1:x2]
                 if cropped.size == 0 or cropped.shape[0] < 10 or cropped.shape[1] < 10:
                     logger.warning(
                         "Skipping region idx=%d (label=%s): crop too small %s",
@@ -918,16 +904,13 @@ class GLMOCREndToEndService(OCRServiceBase):
                 tasks.append((idx, region, cropped, prompt))
 
             if not tasks:
-                raw_content = self._call_vllm(padded, _DEFAULT_PROMPT)
+                raw_content = self._call_vllm(image, _DEFAULT_PROMPT)
                 markdown_content = self._formatter._clean_content(raw_content)
             else:
                 # Parallel OCR calls
                 raw_results: dict[int, str] = {}
                 with ThreadPoolExecutor(max_workers=min(self.max_workers, len(tasks))) as ex:
-                    future_map = {
+                    future_map = {ex.submit(self._call_vllm, cropped, prompt): idx for idx, region, cropped, prompt in tasks}
-                        ex.submit(self._call_vllm, cropped, prompt): idx
-                        for idx, region, cropped, prompt in tasks
-                    }
                     for future in as_completed(future_map):
                         idx = future_map[future]
                         try:
@@ -965,17 +948,3 @@ class GLMOCREndToEndService(OCRServiceBase):
                 logger.warning("Format conversion failed, returning empty latex/mathml/mml: %s", e)
 
         return {"markdown": markdown_content, "latex": latex, "mathml": mathml, "mml": mml}
-
-
-if __name__ == "__main__":
-    mineru_service = MineruOCRService()
-    image = cv2.imread("test/formula2.jpg")
-    image_numpy = np.array(image)
-    # Encode image to bytes (as done in API layer)
-    success, encoded_image = cv2.imencode(".png", image_numpy)
-    if not success:
-        raise RuntimeError("Failed to encode image")
-    image_bytes = BytesIO(encoded_image.tobytes())
-    image_bytes.seek(0)
-    ocr_result = mineru_service.recognize(image_bytes)
-    print(ocr_result)

tests/api/v1/endpoints/test_image_endpoint.py

@@ -1,5 +1,4 @@
 import numpy as np
-import pytest
 from fastapi import FastAPI
 from fastapi.testclient import TestClient
 
@@ -35,7 +34,9 @@ def test_image_endpoint_requires_exactly_one_of_image_url_or_image_base64():
     client = _build_client()
 
     missing = client.post("/ocr", json={})
-    both = client.post("/ocr", json={"image_url": "https://example.com/a.png", "image_base64": "abc"})
+    both = client.post(
+        "/ocr", json={"image_url": "https://example.com/a.png", "image_base64": "abc"}
+    )
 
     assert missing.status_code == 422
     assert both.status_code == 422

tests/services/test_glm_postprocess.py

@@ -57,12 +57,22 @@ def test_merge_formula_numbers_merges_before_and_after_formula():
     before = formatter._merge_formula_numbers(
         [
             {"index": 0, "label": "text", "native_label": "formula_number", "content": "(1)"},
-            {"index": 1, "label": "formula", "native_label": "display_formula", "content": "$$\nx+y\n$$"},
+            {
+                "index": 1,
+                "label": "formula",
+                "native_label": "display_formula",
+                "content": "$$\nx+y\n$$",
+            },
         ]
     )
     after = formatter._merge_formula_numbers(
         [
-            {"index": 0, "label": "formula", "native_label": "display_formula", "content": "$$\nx+y\n$$"},
+            {
+                "index": 0,
+                "label": "formula",
+                "native_label": "display_formula",
+                "content": "$$\nx+y\n$$",
+            },
             {"index": 1, "label": "text", "native_label": "formula_number", "content": "(2)"},
         ]
     )

tests/services/test_layout_detector.py

@@ -23,7 +23,9 @@ def test_detect_applies_postprocess_and_keeps_native_label(monkeypatch):
 
     calls = {}
 
-    def fake_apply_layout_postprocess(boxes, img_size, layout_nms, layout_unclip_ratio, layout_merge_bboxes_mode):
+    def fake_apply_layout_postprocess(
+        boxes, img_size, layout_nms, layout_unclip_ratio, layout_merge_bboxes_mode
+    ):
         calls["args"] = {
             "boxes": boxes,
             "img_size": img_size,
@@ -33,7 +35,9 @@ def test_detect_applies_postprocess_and_keeps_native_label(monkeypatch):
         }
         return [boxes[0], boxes[2]]
 
-    monkeypatch.setattr("app.services.layout_detector.apply_layout_postprocess", fake_apply_layout_postprocess)
+    monkeypatch.setattr(
+        "app.services.layout_detector.apply_layout_postprocess", fake_apply_layout_postprocess
+    )
 
     image = np.zeros((200, 100, 3), dtype=np.uint8)
     info = detector.detect(image)

tests/services/test_layout_postprocess.py

@@ -146,6 +146,4 @@ def test_apply_layout_postprocess_clamps_skips_invalid_and_filters_large_image()
         layout_merge_bboxes_mode=None,
     )
 
-    assert result == [
+    assert result == [{"cls_id": 0, "label": "text", "score": 0.95, "coordinate": [0, 0, 40, 50]}]
-        {"cls_id": 0, "label": "text", "score": 0.95, "coordinate": [0, 0, 40, 50]}
-    ]

tests/services/test_ocr_service.py

@@ -46,7 +46,9 @@ def test_encode_region_returns_decodable_base64_jpeg():
     image[:, :] = [0, 128, 255]
 
     encoded = service._encode_region(image)
-    decoded = cv2.imdecode(np.frombuffer(base64.b64decode(encoded), dtype=np.uint8), cv2.IMREAD_COLOR)
+    decoded = cv2.imdecode(
+        np.frombuffer(base64.b64decode(encoded), dtype=np.uint8), cv2.IMREAD_COLOR
+    )
 
     assert decoded.shape[:2] == image.shape[:2]
 
@@ -71,7 +73,9 @@ def test_call_vllm_builds_messages_and_returns_content():
     assert captured["model"] == "glm-ocr"
     assert captured["max_tokens"] == 1024
     assert captured["messages"][0]["content"][0]["type"] == "image_url"
-    assert captured["messages"][0]["content"][0]["image_url"]["url"].startswith("data:image/jpeg;base64,")
+    assert captured["messages"][0]["content"][0]["image_url"]["url"].startswith(
+        "data:image/jpeg;base64,"
+    )
     assert captured["messages"][0]["content"][1] == {"type": "text", "text": "Formula Recognition:"}
 
 
@@ -98,9 +102,19 @@ def test_recognize_falls_back_to_full_image_when_no_layout_regions(monkeypatch):
 
 def test_recognize_skips_figures_keeps_order_and_postprocesses(monkeypatch):
     regions = [
-        LayoutRegion(type="text", native_label="doc_title", bbox=[0, 0, 10, 10], confidence=0.9, score=0.9),
+        LayoutRegion(
-        LayoutRegion(type="figure", native_label="image", bbox=[10, 10, 20, 20], confidence=0.8, score=0.8),
+            type="text", native_label="doc_title", bbox=[0, 0, 10, 10], confidence=0.9, score=0.9
-        LayoutRegion(type="formula", native_label="display_formula", bbox=[20, 20, 40, 40], confidence=0.95, score=0.95),
+        ),
+        LayoutRegion(
+            type="figure", native_label="image", bbox=[10, 10, 20, 20], confidence=0.8, score=0.8
+        ),
+        LayoutRegion(
+            type="formula",
+            native_label="display_formula",
+            bbox=[20, 20, 40, 40],
+            confidence=0.95,
+            score=0.95,
+        ),
     ]
     service = _build_service(regions=regions)
     image = np.zeros((40, 40, 3), dtype=np.uint8)

tests/tools/layout.py

@@ -0,0 +1,35 @@
+import cv2
+
+from app.core.config import get_settings
+from app.services.layout_detector import LayoutDetector
+
+settings = get_settings()
+
+
+def debug_layout_detector():
+    layout_detector = LayoutDetector()
+    image = cv2.imread("test/image2.png")
+
+    print(f"Image shape: {image.shape}")
+
+    # padded_image = ImageProcessor(padding_ratio=0.15).add_padding(image)
+    layout_info = layout_detector.detect(image)
+
+    # draw the layout info and label
+    for region in layout_info.regions:
+        x1, y1, x2, y2 = region.bbox
+        cv2.putText(
+            image,
+            region.native_label,
+            (int(x1), int(y1)),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            (0, 0, 255),
+            2,
+        )
+        cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2)
+    cv2.imwrite("test/layout_debug.png", image)
+
+
+if __name__ == "__main__":
+    debug_layout_detector()