diff --git a/src/ocrbridge/engines/easyocr/engine.py b/src/ocrbridge/engines/easyocr/engine.py
index bbb83fc..048d875 100644
--- a/src/ocrbridge/engines/easyocr/engine.py
+++ b/src/ocrbridge/engines/easyocr/engine.py
@@ -59,6 +59,16 @@ class EasyOCREngine(OCREngine):
     Uses deep learning models for multilingual OCR with automatic GPU acceleration.
     GPU is automatically detected and used when available, with graceful fallback to CPU.
     Supports 80+ languages with superior accuracy for Asian scripts.
+
+    Thread Safety:
+        This engine is NOT thread-safe. The internal EasyOCR Reader instance is shared
+        and reused across calls for performance. Concurrent calls to process() from
+        multiple threads may cause race conditions or undefined behavior.
+
+        For thread-safe usage, either:
+        - Use a separate EasyOCREngine instance per thread
+        - Serialize access to process() using external locking
+        - Use the ocr-service which handles concurrency via async/await with to_thread()
     """
 
     def __init__(self):
@@ -149,6 +159,19 @@ def process(self, file_path: Path, params: OCREngineParams | None = None) -> str
 
         # Create or recreate reader if languages changed
         if self.reader is None or self._current_languages != easyocr_params.languages:
+            # Release old reader to free GPU memory before creating new one
+            if self.reader is not None:
+                del self.reader
+                self.reader = None
+                # Clear GPU cache if available
+                try:
+                    import torch
+
+                    if torch.cuda.is_available():
+                        torch.cuda.empty_cache()
+                except ImportError:
+                    pass
+
             self.reader = self._create_reader(easyocr_params.languages)
             self._current_languages = easyocr_params.languages
 
@@ -223,17 +246,13 @@ def _process_pdf(self, pdf_path: Path, params: EasyOCRParams) -> str:
             )
 
             # Convert results to HOCR for this page
-            # Save image temporarily to get dimensions
-            with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_file:
-                temp_path = Path(tmp_file.name)
+            # Save image temporarily to get dimensions using secure TemporaryDirectory
+            with tempfile.TemporaryDirectory() as temp_dir:
+                temp_path = Path(temp_dir) / "page.png"
                 image.save(temp_path, format="PNG")
-
-            try:
                 page_hocr = self._to_hocr(results, temp_path)
                 page_hocr_list.append(page_hocr)
-            finally:
-                # Clean up temp file
-                temp_path.unlink(missing_ok=True)
+            # Cleanup happens automatically when context manager exits
 
         # Merge all pages into single HOCR document
         if len(page_hocr_list) == 1:
diff --git a/src/ocrbridge/engines/easyocr/hocr.py b/src/ocrbridge/engines/easyocr/hocr.py
index b272b07..303ecd2 100644
--- a/src/ocrbridge/engines/easyocr/hocr.py
+++ b/src/ocrbridge/engines/easyocr/hocr.py
@@ -5,6 +5,7 @@
 """
 
 from typing import Sequence, TypedDict
+from xml.sax.saxutils import escape as xml_escape
 
 Point2D = tuple[float, float]
 BBox = tuple[int, int, int, int]
@@ -80,8 +81,13 @@ def _group_words_into_lines(easyocr_results: Sequence[EasyOCRResult]) -> list[Li
     heights.sort()
     median_height = heights[len(heights) // 2]
 
+    # Guard against zero height (edge case with certain image types)
+    if median_height == 0:
+        median_height = 1.0  # Use minimum default
+
     # Threshold: words are on same line if y_centers within 50% of median height
-    line_threshold = median_height * 0.5
+    # Use max to ensure a minimum threshold for very small text
+    line_threshold = max(1.0, median_height * 0.5)
 
     # Sort words by vertical position (top to bottom)
     words.sort(key=lambda w: w["y_center"])
@@ -172,14 +178,9 @@ def to_hocr(easyocr_results: Sequence[EasyOCRResult], image_width: int, image_he
             # Convert confidence (0.0-1.0) to percentage (0-100)
             conf_percent = int(word_data["confidence"] * 100)
 
-            # Escape text for XML
-            escaped_text = (
-                word_data["text"]
-                .replace("&", "&")
-                .replace("<", "&lt;")
-                .replace(">", "&gt;")
-                .replace('"', "&quot;")
-                .replace("'", "&apos;")
+            # Escape text for XML using stdlib (more robust than manual replacement)
+            escaped_text = xml_escape(
+                word_data["text"], {'"': "&quot;", "'": "&apos;"}
             )
 
             # Create HOCR word element