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d28e16a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | """
OCR Utilities for document processing
"""
import cv2
import numpy as np
from PIL import Image
class OCRProcessor:
"""Handles OCR processing for images including handwriting detection."""
def __init__(self):
try:
import pytesseract
self.pytesseract = pytesseract
except ImportError:
self.pytesseract = None
print("Warning: pytesseract not available")
def detect_handwriting(self, image):
"""Detect if image contains handwriting."""
try:
# Convert PIL Image to numpy array
img_array = np.array(image)
# Convert to grayscale
if len(img_array.shape) == 3:
gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
else:
gray = img_array
# Apply edge detection
edges = cv2.Canny(gray, 50, 150)
# Count edges
edge_pixels = np.sum(edges > 0)
total_pixels = edges.size
edge_ratio = edge_pixels / total_pixels
# Find contours
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Handwriting typically has more irregular contours
is_handwritten = edge_ratio > 0.05 and len(contours) > 20
return {
'is_handwritten': is_handwritten,
'confidence': edge_ratio * 10, # Normalize to 0-1 range
'edge_ratio': edge_ratio,
'contour_count': len(contours)
}
except Exception as e:
return {
'is_handwritten': False,
'confidence': 0,
'edge_ratio': 0,
'error': str(e)
}
def extract_text(self, image, enhance=True):
"""Extract text from image using standard OCR."""
if not self.pytesseract:
return "OCR not available"
try:
if enhance:
image = self._enhance_image(image)
text = self.pytesseract.image_to_string(image)
return text
except Exception as e:
return f"OCR error: {str(e)}"
def extract_from_handwriting(self, image):
"""Extract text from handwritten image."""
if not self.pytesseract:
return "OCR not available"
try:
# Enhance for handwriting
enhanced = self._enhance_for_handwriting(image)
# Use specific OCR config for handwriting
custom_config = r'--oem 3 --psm 6'
text = self.pytesseract.image_to_string(enhanced, config=custom_config)
return text
except Exception as e:
return f"Handwriting OCR error: {str(e)}"
def extract_text_with_confidence(self, image):
"""Extract text with confidence scores."""
if not self.pytesseract:
return {'text': 'OCR not available', 'confidence': 0, 'word_count': 0}
try:
data = self.pytesseract.image_to_data(image, output_type=self.pytesseract.Output.DICT)
# Filter by confidence
text_parts = []
confidences = []
for i, conf in enumerate(data['conf']):
if int(conf) > 30: # Threshold
text_parts.append(data['text'][i])
confidences.append(int(conf))
text = ' '.join(text_parts)
avg_confidence = np.mean(confidences) if confidences else 0
return {
'text': text,
'confidence': avg_confidence,
'word_count': len(text_parts)
}
except Exception as e:
return {
'text': f"Error: {str(e)}",
'confidence': 0,
'word_count': 0
}
def _enhance_image(self, image):
"""Enhance image for better OCR."""
try:
# Convert PIL to numpy
img_array = np.array(image)
# Convert to grayscale
if len(img_array.shape) == 3:
gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
else:
gray = img_array
# Apply thresholding
_, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# Convert back to PIL
return Image.fromarray(binary)
except Exception:
return image
def _enhance_for_handwriting(self, image):
"""Enhance image specifically for handwriting recognition."""
try:
img_array = np.array(image)
if len(img_array.shape) == 3:
gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
else:
gray = img_array
# Apply adaptive thresholding for handwriting
binary = cv2.adaptiveThreshold(
gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2
)
# Denoise
denoised = cv2.fastNlMeansDenoising(binary)
return Image.fromarray(denoised)
except Exception:
return image
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