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 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

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 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

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) 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 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 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 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 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 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 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 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 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

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 Model Zoo Multi-language Support