diff --git a/src/ocr_pipeline/ocr/__init__.py b/src/ocr_pipeline/ocr/__init__.py
index 7a1acc5..baa4bf0 100644
--- a/src/ocr_pipeline/ocr/__init__.py
+++ b/src/ocr_pipeline/ocr/__init__.py
@@ -1,14 +1,13 @@
 from __future__ import annotations
 
 from .engine import (
-    ImagePreprocessor,
     OCREngine,
     OCRResult,
     PaddleOCREngine,
     TesseractEngine,
-    compute_image_hash,
 )
 from .parallel import ParallelProcessor, ProcessedImage
+from .preprocess import ImagePreprocessor, compute_image_hash
 
 __all__ = [
     "OCREngine",
diff --git a/src/ocr_pipeline/ocr/preprocess.py b/src/ocr_pipeline/ocr/preprocess.py
new file mode 100644
index 0000000..74075a9
--- /dev/null
+++ b/src/ocr_pipeline/ocr/preprocess.py
@@ -0,0 +1,119 @@
+from __future__ import annotations
+import hashlib
+import cv2
+import numpy as np
+
+from ocr_pipeline.config import settings
+from ocr_pipeline.utils.logging import get_logger
+
+logger = get_logger(__name__)
+
+
+class ImagePreprocessor:
+    """Image preprocessing for OCR optimization."""
+
+    def __init__(self):
+        self.config = settings.ocr.preprocess
+        self.max_dim = self.config.max_dimension
+
+    def load_image(self, path: str) -> np.ndarray:
+        """Load image from file path."""
+        image = cv2.imread(str(path))
+        if image is None:
+            raise ValueError(f"Failed to load image: {path}")
+        return image
+
+    def resize_if_needed(self, image: np.ndarray) -> np.ndarray:
+        """Resize image if it exceeds max dimension."""
+        h, w = image.shape[:2]
+        if max(h, w) > self.max_dim:
+            scale = self.max_dim / max(h, w)
+            new_w, new_h = int(w * scale), int(h * scale)
+            image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA)
+        return image
+
+    def deskew(self, image: np.ndarray) -> np.ndarray:
+        """Correct skew in the image."""
+        if not self.config.deskew:
+            return image
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        coords = np.column_stack(np.where(gray > 0))
+        if len(coords) == 0:
+            return image
+        angle = cv2.minAreaRect(coords)[-1]
+        if angle < -45:
+            angle = -(90 + angle)
+        else:
+            angle = -angle
+        if abs(angle) > 0.5:
+            h, w = image.shape[:2]
+            center = (w // 2, h // 2)
+            M = cv2.getRotationMatrix2D(center, angle, 1.0)
+            image = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
+        return image
+
+    def denoise(self, image: np.ndarray) -> np.ndarray:
+        """Remove noise from image."""
+        if not self.config.denoise:
+            return image
+        return cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)
+
+    def apply_clahe(self, image: np.ndarray) -> np.ndarray:
+        """Apply CLAHE for contrast enhancement."""
+        if not self.config.clahe:
+            return image
+        lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
+        l, a, b = cv2.split(lab)
+        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+        l = clahe.apply(l)
+        lab = cv2.merge((l, a, b))
+        return cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
+
+    def remove_lines(self, image: np.ndarray) -> np.ndarray:
+        """Remove horizontal and vertical lines (tables, grids)."""
+        if not self.config.remove_lines:
+            return image
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+        horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40, 1))
+        vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 40))
+        horizontal_lines = cv2.morphologyEx(binary, cv2.MORPH_OPEN, horizontal_kernel)
+        vertical_lines = cv2.morphologyEx(binary, cv2.MORPH_OPEN, vertical_kernel)
+        lines = cv2.bitwise_or(horizontal_lines, vertical_lines)
+        contours, _ = cv2.findContours(lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        mask = np.ones(image.shape[:2], dtype=np.uint8) * 255
+        cv2.drawContours(mask, contours, -1, 0, thickness=cv2.FILLED)
+        result = image.copy()
+        result[mask == 0] = [255, 255, 255]
+        return result
+
+    def adaptive_threshold(self, image: np.ndarray) -> np.ndarray:
+        """Apply adaptive thresholding for binarization."""
+        if not self.config.adaptive_threshold:
+            return image
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        binary = cv2.adaptiveThreshold(
+            gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+            cv2.THRESH_BINARY, 11, 2
+        )
+        return cv2.cvtColor(binary, cv2.COLOR_GRAY2BGR)
+
+    def preprocess(self, path: str) -> np.ndarray:
+        """Full preprocessing pipeline."""
+        image = self.load_image(path)
+        image = self.resize_if_needed(image)
+        image = self.deskew(image)
+        image = self.denoise(image)
+        image = self.apply_clahe(image)
+        image = self.remove_lines(image)
+        image = self.adaptive_threshold(image)
+        return image
+
+
+def compute_image_hash(path: str) -> str:
+    """Compute SHA256 hash of image file for deduplication."""
+    hasher = hashlib.sha256()
+    with open(path, "rb") as f:
+        for chunk in iter(lambda: f.read(8192), b""):
+            hasher.update(chunk)
+    return hasher.hexdigest()