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Lightweight OCR Engine using EasyOCR
Optimized for CPU inference with high accuracy
Supports invoices, receipts, and forms
"""
# Fix for TensorFlow/PaddlePaddle mutex warnings on macOS
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['OPENBLAS_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'
os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
os.environ['NUMEXPR_NUM_THREADS'] = '1'
# Suppress TensorFlow warnings
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=FutureWarning)
import numpy as np
from typing import List, Dict, Tuple, Optional
import logging
import easyocr
import json
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class LightweightOCR:
"""
Lightweight OCR engine based on EasyOCR
Optimized for CPU inference and document processing
"""
def __init__(
self,
lang: str = 'en',
use_gpu: bool = False,
use_angle_cls: bool = True
):
"""
Initialize EasyOCR engine
Args:
lang: Language code ('en' for English)
use_gpu: Use GPU if available (False for CPU-only deployment)
use_angle_cls: Not used in EasyOCR (kept for compatibility)
"""
logger.info(f"Initializing EasyOCR (language: {lang}, GPU: {use_gpu})")
try:
# Initialize EasyOCR Reader
# EasyOCR supports multiple languages, here we use English
self.reader = easyocr.Reader(['en'], gpu=use_gpu)
logger.info("EasyOCR initialized successfully")
except Exception as e:
logger.error(f"EasyOCR initialization failed: {str(e)}")
raise
def extract_text(
self,
image: np.ndarray,
return_boxes: bool = True,
confidence_threshold: float = 0.5
) -> Dict:
"""
Extract text from document image
Args:
image: Input image as numpy array (BGR or RGB)
return_boxes: Include bounding boxes in output
confidence_threshold: Minimum confidence score to include results
Returns:
Dictionary containing:
- text: Full extracted text
- boxes: List of word/line detections with boxes and confidence
- lines: Grouped by lines
"""
logger.info("Starting OCR extraction")
# Run EasyOCR
# detail=1 returns [box, text, confidence]
results = self.reader.readtext(image, detail=1)
if not results:
logger.warning("No text detected in image")
return {
"text": "",
"boxes": [],
"lines": []
}
# Parse results
full_text_parts = []
boxes = []
lines = []
for idx, (box_coords, text, confidence) in enumerate(results):
# Filter by confidence
if confidence < confidence_threshold:
continue
# Add to full text
full_text_parts.append(text)
# Convert box to simple bbox format [x1, y1, x2, y2]
# EasyOCR returns [[x1,y1], [x2,y1], [x2,y2], [x1,y2]]
box_array = np.array(box_coords)
x_coords = box_array[:, 0]
y_coords = box_array[:, 1]
bbox = [
float(np.min(x_coords)),
float(np.min(y_coords)),
float(np.max(x_coords)),
float(np.max(y_coords))
]
# Create box entry
box_entry = {
"text": text,
"bbox": bbox,
"confidence": float(confidence),
"line_number": idx
}
boxes.append(box_entry)
# Create line entry
line_entry = {
"text": text,
"bbox": bbox,
"confidence": float(confidence)
}
lines.append(line_entry)
# Combine full text
full_text = "\n".join(full_text_parts)
result_dict = {
"text": full_text,
"boxes": boxes if return_boxes else [],
"lines": lines
}
logger.info(f"Extracted {len(lines)} lines with avg confidence: "
f"{np.mean([l['confidence'] for l in lines]) if lines else 0:.3f}")
return result_dict
def get_text_with_positions(
self,
image: np.ndarray,
confidence_threshold: float = 0.5
) -> Tuple[str, List[Dict]]:
"""
Convenience method to get both text and position information
Args:
image: Input image
confidence_threshold: Minimum confidence
Returns:
Tuple of (full_text, list of box dictionaries)
"""
result = self.extract_text(image, return_boxes=True, confidence_threshold=confidence_threshold)
return result["text"], result["boxes"]
class PDFtoImageConverter:
"""
Convert PDF pages to images for OCR processing
Lightweight implementation
"""
@staticmethod
def pdf_to_images(pdf_path: str, dpi: int = 200) -> List[np.ndarray]:
"""
Convert PDF to list of images
Args:
pdf_path: Path to PDF file
dpi: Resolution for conversion (200 is good for OCR)
Returns:
List of images as numpy arrays
"""
try:
from pdf2image import convert_from_path
import cv2
logger.info(f"Converting PDF to images: {pdf_path}")
# Convert PDF to PIL images
pil_images = convert_from_path(pdf_path, dpi=dpi)
# Convert to numpy arrays (OpenCV format)
images = []
for pil_img in pil_images:
# Convert PIL RGB to OpenCV BGR
img_array = np.array(pil_img)
img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
images.append(img_bgr)
logger.info(f"Converted {len(images)} pages")
return images
except ImportError:
logger.error("pdf2image not installed. Install with: pip install pdf2image")
logger.error("Also requires poppler-utils system package")
raise
except Exception as e:
logger.error(f"Error converting PDF: {str(e)}")
raise
def extract_text_from_file(
file_path: str,
lang: str = 'en',
use_gpu: bool = False,
confidence_threshold: float = 0.5
) -> Dict:
"""
Convenience function to extract text from image or PDF file
Args:
file_path: Path to image or PDF file
lang: Language code
use_gpu: Use GPU for OCR
confidence_threshold: Minimum confidence score
Returns:
Dictionary with extracted text and metadata
"""
import cv2
import os
# Initialize OCR
ocr_engine = LightweightOCR(lang=lang, use_gpu=use_gpu)
# Check file type
ext = os.path.splitext(file_path)[1].lower()
if ext == '.pdf':
# Convert PDF to images
images = PDFtoImageConverter.pdf_to_images(file_path)
# Process each page
results = []
for page_num, image in enumerate(images):
logger.info(f"Processing page {page_num + 1}/{len(images)}")
page_result = ocr_engine.extract_text(image, confidence_threshold=confidence_threshold)
page_result['page_number'] = page_num + 1
results.append(page_result)
return {
"file_path": file_path,
"file_type": "pdf",
"num_pages": len(images),
"pages": results
}
else:
# Load image
image = cv2.imread(file_path)
if image is None:
raise ValueError(f"Could not load image from {file_path}")
# Process single image
result = ocr_engine.extract_text(image, confidence_threshold=confidence_threshold)
return {
"file_path": file_path,
"file_type": "image",
"num_pages": 1,
"pages": [result]
}
if __name__ == "__main__":
import sys
if len(sys.argv) < 2:
print("Usage: python ocr_engine.py <image_or_pdf_path>")
sys.exit(1)
input_path = sys.argv[1]
output_path = "ocr_output.json"
# Extract text
result = extract_text_from_file(input_path)
# Save to JSON
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(result, f, indent=2, ensure_ascii=False)
print(f"\nOCR results saved to {output_path}")
print(f"\nExtracted text preview:")
print("-" * 50)
for page in result['pages']:
print(page['text'][:500]) # First 500 chars
if len(page['text']) > 500:
print("...")
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