Skill: duykhangdangzn1/smar (version 1.0.0)

--- name: smart-ocr description: Extract text from images and scanned documents using PaddleOCR - supports 100+ languages author: claude-office-skills version: "1.0" tags: [ocr, paddleocr, text-extraction, multilingual, image] models: [claude-sonnet-4, claude-opus-4] tools: [computer, code_execution, file_operations] library: name: PaddleOCR url: https://github.com/PaddlePaddle/PaddleOCR stars: 69k --- # 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 1. Provide the image or scanned document 2. Optionally specify language(s) to detect 3. 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 engine ocr = PaddleOCR(use_angle_cls=True, lang='en') # Run OCR on image result = 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 codes languages = { '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 language ocr = PaddleOCR(lang='ch') # Chinese ocr = PaddleOCR(lang='japan') # Japanese ocr = PaddleOCR(lang='multilingual') # Auto-detect ``` ### Configuration 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 image result = ocr.ocr('image.png') # Multiple images images = ['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 requests from io import BytesIO # From URL response = requests.get('https://example.com/image.png') result = ocr.ocr(BytesIO(response.content)) # From bytes with 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 1. **Preprocess Images**: Improve quality before OCR 2. **Choose Correct Language**: Specify language for better accuracy 3. **Handle Multi-column**: Process columns separately 4. **Filter Low Confidence**: Skip results below threshold 5. **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_path ``` ### Batch OCR with Progress ```python from tqdm import tqdm from 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 results ``` ## Examples ### Example 1: Business Card Reader ```python from paddleocr import PaddleOCR import 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 PaddleOCR import 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 version pip install paddlepaddle paddleocr # GPU version (CUDA 11.x) pip install paddlepaddle-gpu paddleocr # Additional dependencies pip install pdf2image Pillow ``` ## Resources - [PaddleOCR GitHub](https://github.com/PaddlePaddle/PaddleOCR) - [Model Zoo](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.7/doc/doc_en/models_list_en.md) - [Multi-language Support](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.7/doc/doc_en/multi_languages_en.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)