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smart-ocr

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🇺🇸

Original

English
🇨🇳

Translation

Chinese

Smart OCR Skill

智能OCR Skill

Overview

概述

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.
本Skill借助PaddleOCR（一款支持100余种语言的领先OCR引擎）实现从图片和扫描文档中智能提取文本。可高精度提取照片、截图、扫描PDF及手写文档中的文本。

How to Use

使用方法

  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"
  1. 提供图片或扫描文档
  2. 可选择性指定要检测的语言
  3. 我将提取包含位置和置信度数据的文本
示例提示词：
  • "提取此截图中的所有文本"
  • "对这份扫描PDF文档进行OCR识别"
  • "读取这张名片照片中的文本"
  • "提取此图片中的中英文文本"

Domain Knowledge

领域知识

PaddleOCR Fundamentals

PaddleOCR基础

python
from paddleocr import PaddleOCR
python
from paddleocr import PaddleOCR

Initialize OCR engine

Initialize OCR engine

ocr = PaddleOCR(use_angle_cls=True, lang='en')
ocr = PaddleOCR(use_angle_cls=True, lang='en')

Run OCR on image

Run OCR on image

result = ocr.ocr('image.png', cls=True)
result = ocr.ocr('image.png', cls=True)

Result structure: [[box, (text, confidence)], ...]

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})")
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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})")
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Supported Languages

支持的语言

python
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python
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Common language codes

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 }
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

Use specific language

ocr = PaddleOCR(lang='ch') # Chinese ocr = PaddleOCR(lang='japan') # Japanese ocr = PaddleOCR(lang='multilingual') # Auto-detect
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ocr = PaddleOCR(lang='ch') # Chinese ocr = PaddleOCR(lang='japan') # Japanese ocr = PaddleOCR(lang='multilingual') # Auto-detect
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Configuration Options

配置选项

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
)
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

处理不同来源

Image Files

图片文件

python
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python
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Single image

Single image

result = ocr.ocr('image.png')
result = ocr.ocr('image.png')

Multiple images

Multiple images

images = ['img1.png', 'img2.png', 'img3.png'] for img in images: result = ocr.ocr(img) process_result(result)
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images = ['img1.png', 'img2.png', 'img3.png'] for img in images: result = ocr.ocr(img) process_result(result)
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PDF Files (Scanned)

扫描PDF文件

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)
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

URL与字节流

python
import requests
from io import BytesIO
python
import requests
from io import BytesIO

From URL

From URL

response = requests.get('https://example.com/image.png') result = ocr.ocr(BytesIO(response.content))
response = requests.get('https://example.com/image.png') result = ocr.ocr(BytesIO(response.content))

From bytes

From bytes

with open('image.png', 'rb') as f: img_bytes = f.read() result = ocr.ocr(BytesIO(img_bytes))
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with open('image.png', 'rb') as f: img_bytes = f.read() result = ocr.ocr(BytesIO(img_bytes))
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Result Processing

结果处理

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
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)

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']))
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def sort_by_position(lines): return sorted(lines, key=lambda x: (x['bbox']['top'], x['bbox']['left']))
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Text Layout Reconstruction

文本布局重建

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)
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

最佳实践

  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
  1. 预处理图片：在OCR识别前提升图片质量
  2. 选择正确语言：指定语言以提升识别精度
  3. 处理多列布局：单独处理每一列内容
  4. 过滤低置信度结果：跳过低于阈值的识别结果
  5. 批量处理：高效处理多张图片

Common Patterns

常见应用场景

Image Preprocessing

图片预处理

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
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

带进度的批量OCR

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
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

示例

Example 1: Business Card Reader

示例1：名片识别器

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']}")
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

示例2：收据扫描器

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']}")
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

示例3：多语言文档识别

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})")
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

局限性

  • 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
  • 手写文本的识别精度参差不齐
  • 极小文本可能无法被识别
  • 复杂背景会降低识别精度
  • 旋转文本需要开启角度分类功能
  • 推荐使用GPU以获得最佳性能

Installation

安装

bash
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bash
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CPU version

CPU version

pip install paddlepaddle paddleocr
pip install paddlepaddle paddleocr

GPU version (CUDA 11.x)

GPU version (CUDA 11.x)

pip install paddlepaddle-gpu paddleocr
pip install paddlepaddle-gpu paddleocr

Additional dependencies

Additional dependencies

pip install pdf2image Pillow
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pip install pdf2image Pillow
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Resources

参考资源