Smart OCR Skill
Overview
This skill enables intelligent text extraction from images and scanned documents using PaddleOCR - a leading OCR engine supporting 100+ languages. Extract text from photos, screenshots, scanned PDFs, and handwritten documents with high accuracy.
How to Use
- Provide the image or scanned document
- Optionally specify language(s) to detect
- I'll extract text with position and confidence data
Example prompts:
- "Extract all text from this screenshot"
- "OCR this scanned PDF document"
- "Read the text from this business card photo"
- "Extract Chinese and English text from this image"
Domain Knowledge
PaddleOCR Fundamentals
python
from paddleocr import PaddleOCR
# Initialize OCR engineocr = PaddleOCR(use_angle_cls=True, lang='en')
# Run OCR on imageresult = ocr.ocr('image.png', cls=True)
# Result structure: [[box, (text, confidence)], ...]for line in result[0]: box = line[0] # [[x1,y1], [x2,y2], [x3,y3], [x4,y4]] text = line[1][0] # Extracted text conf = line[1][1] # Confidence score print(f"{text} ({conf:.2f})")Supported Languages
python
# Common language codeslanguages = { 'en': 'English', 'ch': 'Chinese (Simplified)', 'cht': 'Chinese (Traditional)', 'japan': 'Japanese', 'korean': 'Korean', 'french': 'French', 'german': 'German', 'spanish': 'Spanish', 'russian': 'Russian', 'arabic': 'Arabic', 'hindi': 'Hindi', 'vi': 'Vietnamese', 'th': 'Thai', # ... 100+ languages supported}
# Use specific languageocr = PaddleOCR(lang='ch') # Chineseocr = PaddleOCR(lang='japan') # Japaneseocr = PaddleOCR(lang='multilingual') # Auto-detectConfiguration Options
python
from paddleocr import PaddleOCR
ocr = PaddleOCR( # Detection settings det_model_dir=None, # Custom detection model det_limit_side_len=960, # Max side length for detection det_db_thresh=0.3, # Binarization threshold det_db_box_thresh=0.5, # Box score threshold # Recognition settings rec_model_dir=None, # Custom recognition model rec_char_dict_path=None, # Custom character dictionary # Angle classification use_angle_cls=True, # Enable angle classification cls_model_dir=None, # Custom classification model # Language lang='en', # Language code # Performance use_gpu=True, # Use GPU if available gpu_mem=500, # GPU memory limit (MB) enable_mkldnn=True, # CPU optimization # Output show_log=False, # Suppress logs)Processing Different Sources
Image Files
python
# Single imageresult = ocr.ocr('image.png')
# Multiple imagesimages = ['img1.png', 'img2.png', 'img3.png']for img in images: result = ocr.ocr(img) process_result(result)PDF Files (Scanned)
python
from pdf2image import convert_from_path
def ocr_pdf(pdf_path): """OCR a scanned PDF.""" # Convert PDF pages to images images = convert_from_path(pdf_path) all_text = [] for i, img in enumerate(images): # Save temp image temp_path = f'temp_page_{i}.png' img.save(temp_path) # OCR the image result = ocr.ocr(temp_path) # Extract text page_text = '\n'.join([line[1][0] for line in result[0]]) all_text.append(f"--- Page {i+1} ---\n{page_text}") os.remove(temp_path) return '\n\n'.join(all_text)URLs and Bytes
python
import requestsfrom io import BytesIO
# From URLresponse = requests.get('https://example.com/image.png')result = ocr.ocr(BytesIO(response.content))
# From byteswith open('image.png', 'rb') as f: img_bytes = f.read()result = ocr.ocr(BytesIO(img_bytes))Result Processing
python
def process_ocr_result(result): """Process OCR result into structured data.""" lines = [] for line in result[0]: box = line[0] text = line[1][0] confidence = line[1][1] # Calculate bounding box x_coords = [p[0] for p in box] y_coords = [p[1] for p in box] lines.append({ 'text': text, 'confidence': confidence, 'bbox': { 'left': min(x_coords), 'top': min(y_coords), 'right': max(x_coords), 'bottom': max(y_coords), }, 'raw_box': box }) return lines
# Sort by position (top to bottom, left to right)def sort_by_position(lines): return sorted(lines, key=lambda x: (x['bbox']['top'], x['bbox']['left']))Text Layout Reconstruction
python
def reconstruct_layout(result, line_threshold=10): """Reconstruct text layout from OCR results.""" lines = process_ocr_result(result) lines = sort_by_position(lines) # Group into logical lines text_lines = [] current_line = [] current_y = None for line in lines: y = line['bbox']['top'] if current_y is None or abs(y - current_y) < line_threshold: current_line.append(line) current_y = y else: # New line text_lines.append(' '.join([l['text'] for l in current_line])) current_line = [line] current_y = y # Add last line if current_line: text_lines.append(' '.join([l['text'] for l in current_line])) return '\n'.join(text_lines)Best Practices
- Preprocess Images: Improve quality before OCR
- Choose Correct Language: Specify language for better accuracy
- Handle Multi-column: Process columns separately
- Filter Low Confidence: Skip results below threshold
- Batch Processing: Process multiple images efficiently
Common Patterns
Image Preprocessing
python
from PIL import Image, ImageEnhance, ImageFilter
def preprocess_image(image_path): """Preprocess image for better OCR.""" img = Image.open(image_path) # Convert to grayscale img = img.convert('L') # Enhance contrast enhancer = ImageEnhance.Contrast(img) img = enhancer.enhance(2.0) # Sharpen img = img.filter(ImageFilter.SHARPEN) # Save preprocessed preprocessed_path = 'preprocessed.png' img.save(preprocessed_path) return preprocessed_pathBatch OCR with Progress
python
from tqdm import tqdmfrom concurrent.futures import ThreadPoolExecutor
def batch_ocr(image_paths, max_workers=4): """OCR multiple images in parallel.""" results = {} def process_single(img_path): result = ocr.ocr(img_path) return img_path, result with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [executor.submit(process_single, p) for p in image_paths] for future in tqdm(futures, desc="Processing OCR"): path, result = future.result() results[path] = result return resultsExamples
Example 1: Business Card Reader
python
from paddleocr import PaddleOCRimport re
def read_business_card(image_path): """Extract contact info from business card.""" ocr = PaddleOCR(use_angle_cls=True, lang='en') result = ocr.ocr(image_path) # Extract all text all_text = [] for line in result[0]: all_text.append(line[1][0]) full_text = '\n'.join(all_text) # Parse contact info contact = { 'name': None, 'email': None, 'phone': None, 'company': None, 'title': None, 'raw_text': full_text } # Email pattern email_match = re.search(r'[\w\.-]+@[\w\.-]+\.\w+', full_text) if email_match: contact['email'] = email_match.group() # Phone pattern phone_match = re.search(r'[\+\d][\d\s\-\(\)]{8,}', full_text) if phone_match: contact['phone'] = phone_match.group().strip() # Name is usually the largest/first text if all_text: contact['name'] = all_text[0] return contact
card_info = read_business_card('business_card.jpg')print(f"Name: {card_info['name']}")print(f"Email: {card_info['email']}")print(f"Phone: {card_info['phone']}")Example 2: Receipt Scanner
python
from paddleocr import PaddleOCRimport re
def scan_receipt(image_path): """Extract items and total from receipt.""" ocr = PaddleOCR(use_angle_cls=True, lang='en') result = ocr.ocr(image_path) lines = [] for line in result[0]: text = line[1][0] y_pos = line[0][0][1] lines.append({'text': text, 'y': y_pos}) # Sort by vertical position lines.sort(key=lambda x: x['y']) receipt = { 'items': [], 'subtotal': None, 'tax': None, 'total': None } for line in lines: text = line['text'] # Look for total if 'total' in text.lower(): amount = re.search(r'\$?([\d,]+\.?\d*)', text) if amount: if 'sub' in text.lower(): receipt['subtotal'] = float(amount.group(1).replace(',', '')) else: receipt['total'] = float(amount.group(1).replace(',', '')) # Look for tax elif 'tax' in text.lower(): amount = re.search(r'\$?([\d,]+\.?\d*)', text) if amount: receipt['tax'] = float(amount.group(1).replace(',', '')) # Look for items (line with price) else: item_match = re.search(r'(.+?)\s+\$?([\d,]+\.?\d+)$', text) if item_match: receipt['items'].append({ 'name': item_match.group(1).strip(), 'price': float(item_match.group(2).replace(',', '')) }) return receipt
receipt_data = scan_receipt('receipt.jpg')print(f"Items: {len(receipt_data['items'])}")print(f"Total: ${receipt_data['total']}")Example 3: Multi-language Document
python
from paddleocr import PaddleOCR
def ocr_multilingual(image_path, languages=['en', 'ch']): """OCR document with multiple languages.""" all_results = {} for lang in languages: ocr = PaddleOCR(use_angle_cls=True, lang=lang) result = ocr.ocr(image_path) texts = [] for line in result[0]: texts.append({ 'text': line[1][0], 'confidence': line[1][1] }) all_results[lang] = texts # Merge results, keeping highest confidence merged = {} for lang, texts in all_results.items(): for item in texts: text = item['text'] conf = item['confidence'] if text not in merged or merged[text]['confidence'] < conf: merged[text] = {'confidence': conf, 'language': lang} return merged
result = ocr_multilingual('bilingual_document.png')for text, info in result.items(): print(f"[{info['language']}] {text} ({info['confidence']:.2f})")Limitations
- Handwritten text accuracy varies
- Very small text may not be detected
- Complex backgrounds reduce accuracy
- Rotated text needs angle classification
- GPU recommended for best performance
Installation
bash
# CPU versionpip install paddlepaddle paddleocr
# GPU version (CUDA 11.x)pip install paddlepaddle-gpu paddleocr
# Additional dependenciespip install pdf2image Pillow
