Building an OCR Form Data Extractor: From Zero to Hero

Extract text from handwritten forms automatically using Python, EasyOCR, and OpenCV

📌 Project Overview

Ever wondered how apps like Google Lens extract text from images? In this tutorial, we’ll build a Smart Form Data Extractor that can read printed AND handwritten text from form images—perfect for digitizing student records, surveys, or any paper forms!

What You’ll Build:

Single image processing mode
Batch processing for multiple forms
Multi-format output (Text, JSON, Excel)
Bilingual support (English + Tamil)
Smart field detection (phone numbers, dates, pincodes)

Time Required: 1.5 – 2 hours
Difficulty: Beginner-Friendly
Cost: 100% Free (using Google Colab)

🎯 What We’re Solving

The Problem: Training institutes, schools, and businesses receive hundreds of paper forms daily. Manual data entry is:

⏰ Time-consuming (5-10 minutes per form)
😫 Tedious and boring
❌ Error-prone (typos, missed fields)
💰 Expensive (hiring data entry staff)

Our Solution: An automated OCR system that:

✅ Processes forms in 30-60 seconds
✅ Extracts ALL text automatically
✅ Outputs organized data in multiple formats
✅ Handles both printed and handwritten text

🛠️ Tech Stack

Technology	Purpose	Why This One?
Python	Programming language	Industry standard for AI/ML
EasyOCR	Text recognition engine	Best for handwriting, supports 80+ languages
OpenCV	Image processing	Improves image quality for better OCR
Pandas	Data manipulation	Easy Excel/CSV export
Google Colab	Development environment	Free, no setup required, includes GPU

No Installation Required! Everything runs in your browser via Google Colab.

📚 Understanding the Fundamentals

Before diving into code, let’s understand key concepts:

What is OCR (Optical Character Recognition)?

Simple Definition: Converting images of text into actual, editable text.

Real-World Analogy: Imagine you take a photo of a book page. Your eyes can READ the text, but your phone only sees it as a picture. OCR is like teaching your computer to “read” text from images just like you do!

How OCR Works (3 Steps):

Step 1: Image Preprocessing
    ↓ (Clean up image: remove noise, increase contrast)
Step 2: Text Detection
    ↓ (Find WHERE text is located in the image)
Step 3: Text Recognition
    ↓ (Identify WHAT each character is)
Result: Editable Text!

Why Handwriting Recognition is Harder

Printed Text vs Handwritten Text:

Aspect	Printed Text	Handwritten Text
Consistency	Always same font	Everyone writes differently
Clarity	Sharp, clean lines	Can be messy, unclear
Spacing	Perfect spacing	Irregular spacing
OCR Accuracy	98-99%	80-95% (depends on writing quality)

That’s why we use EasyOCR – it’s trained specifically to handle handwriting variations!

Understanding Confidence Scores

When OCR reads text, it gives a confidence score (0.0 to 1.0):

0.9 - 1.0 = Very confident (probably correct) ✅
0.7 - 0.9 = Moderately confident (likely correct) ⚠️
0.5 - 0.7 = Low confidence (might be wrong) ⚠️
0.0 - 0.5 = Very uncertain (probably wrong) ❌

Pro Tip: We’ll filter out results with confidence < 0.3 to avoid garbage data.

🚀 Project Setup

Step 1: Open Google Colab

Go to Google Colab
Sign in with your Google account
Click “New Notebook”
Rename it: Form_Data_Extractor.ipynb

Why Colab?

✅ No installation needed
✅ Free GPU access
✅ Easy file upload/download
✅ Share-able notebooks

Step 2: Install Required Libraries

Copy and paste this into your first code cell:

# Installation Cell - Run this FIRST (takes 2-3 minutes)
# Only needs to be run once per session

!pip install easyocr
!pip install opencv-python-headless
!pip install pytesseract
!apt-get install tesseract-ocr
!apt-get install tesseract-ocr-tam  # Tamil language support

print("✅ All libraries installed successfully!")

What each library does:

EasyOCR: Main OCR engine (reads text from images)
OpenCV: Image processing (cleans and prepares images)
Pytesseract: Backup OCR engine (good for printed text)
Tesseract-OCR: OCR engine core
Tesseract-TAM: Tamil language support

Expected Output:

✅ All libraries installed successfully!

⏰ Time: 2-3 minutes on first run

📦 Building the Project: Step-by-Step

Step 3: Import Libraries

Create a new code cell and add:

# Import all required libraries
import easyocr                    # OCR engine
import cv2                        # OpenCV for image processing
import numpy as np                # Numerical operations
import pandas as pd               # Data handling (Excel export)
import json                       # JSON format support
import re                         # Regular expressions (pattern matching)
from PIL import Image             # Image display
import os                         # File operations
from datetime import datetime     # Date/time handling
from google.colab import files    # File upload in Colab

print("✅ All libraries imported successfully!")
print("=" * 80)

Why we need each one:

easyocr → Main text recognition
cv2 → Image preprocessing (grayscale, thresholding)
numpy → Mathematical operations on images
pandas → Organize data into Excel/CSV
json → Export as JSON format
re → Find patterns (phone numbers, pincodes)
PIL → Display images in notebook
os → Handle file paths
datetime → Timestamp our extractions
files → Upload forms from your computer

Step 4: Initialize OCR Reader

# Initialize EasyOCR Reader
# This downloads language models (takes 1-2 minutes first time)

print("🔄 Initializing OCR Reader (English + Tamil)...")
print("⏳ First run may take 1-2 minutes (downloading models)...")

# Create reader for English and Tamil
# gpu=False because Colab free tier may not have GPU
reader = easyocr.Reader(['en', 'ta'], gpu=False)

print("✅ OCR Reader initialized and ready!")
print("=" * 80)

What’s happening:

Downloads pre-trained models for English and Tamil
Models are ~100MB each
Only downloads once, then cached
gpu=False uses CPU (works on free Colab)

Real-World Analogy: Like installing a language pack on your phone – once installed, it works offline!

Step 5: Image Preprocessing Functions

Why preprocessing? Raw images often have:

Poor lighting
Background noise
Low contrast
Blur or shadows

Preprocessing fixes these issues for better OCR accuracy!

def preprocess_image(image_path):
    """
    Improves image quality for better OCR results
    
    Steps:
    1. Convert to grayscale (removes color, keeps text)
    2. Apply thresholding (makes text pure black, background pure white)
    3. Remove noise (erases tiny dots/marks)
    """
    
    # Read the image
    img = cv2.imread(image_path)
    
    # Convert to grayscale
    # Why? OCR works better with black text on white background
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    # Apply Otsu's thresholding
    # Automatically finds best threshold value
    # Converts to binary image (pure black and white)
    _, thresh = cv2.threshold(gray, 0, 255, 
                              cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    
    # Remove small noise using morphological operations
    kernel = np.ones((1, 1), np.uint8)
    processed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
    
    return processed


def display_image(image_path):
    """Display the uploaded image for visual confirmation"""
    from IPython.display import display
    
    img = Image.open(image_path)
    display(img)
    
    print(f"✅ Image loaded: {image_path}")
    print(f"📐 Dimensions: {img.size[0]} x {img.size[1]} pixels")
    print("=" * 80)

print("✅ Preprocessing functions created!")

Before vs After Preprocessing:

BEFORE:                    AFTER:
[Gray, noisy image]   →   [Clean, black text on white]
[Low contrast]        →   [High contrast]
[Background texture]  →   [Pure white background]

Step 6: Text Extraction Function (The Core!)

This is where the magic happens!

def extract_text_from_image(image_path):
    """
    Extract all text from the form image
    
    Returns:
    - raw_text: Complete text as one string
    - structured_data: List with text, position, confidence
    """
    
    print(f"🔍 Processing: {image_path}")
    print("⏳ Extracting text... (30-60 seconds)")
    print("=" * 80)
    
    # Use EasyOCR to read the image
    # Returns: [([[x1,y1], [x2,y2], [x3,y3], [x4,y4]], 'text', confidence), ...]
    result = reader.readtext(image_path)
    
    # Initialize storage
    raw_text = []
    structured_data = []
    
    print(f"✅ Detected {len(result)} text elements")
    
    # Process each detected text
    for detection in result:
        bbox, text, confidence = detection
        
        # bbox = coordinates where text is located
        # text = the extracted text
        # confidence = how sure OCR is (0.0 to 1.0)
        
        # Filter: Only keep text with confidence > 30%
        if confidence > 0.3:
            raw_text.append(text)
            
            # Get position (top-left corner)
            x = int(bbox[0][0])
            y = int(bbox[0][1])
            
            structured_data.append({
                'text': text,
                'x': x,
                'y': y,
                'confidence': round(confidence, 2)
            })
    
    # Join all text with newlines
    complete_text = '\n'.join(raw_text)
    
    print(f"✅ Successfully extracted {len(structured_data)} high-confidence texts")
    print("=" * 80)
    
    return complete_text, structured_data

print("✅ Text extraction function created!")

Understanding the Output:

# Example output structure:
structured_data = [
    {
        'text': 'R.ABDUL RAHEEM',
        'x': 245,
        'y': 112,
        'confidence': 0.94
    },
    {
        'text': '9680387400',
        'x': 412,
        'y': 450,
        'confidence': 0.98
    },
    # ... more text elements
]

Step 7: Smart Pattern Extraction

Extract specific information using patterns:

def extract_phone_numbers(text):
    """
    Extract all 10-digit phone numbers
    Pattern: Exactly 10 consecutive digits
    """
    phone_pattern = r'\b\d{10}\b'
    phones = re.findall(phone_pattern, text)
    return phones


def extract_pincode(text):
    """
    Extract 6-digit Indian pincode
    Pattern: Exactly 6 consecutive digits
    """
    pincode_pattern = r'\b\d{6}\b'
    pincodes = re.findall(pincode_pattern, text)
    return pincodes[0] if pincodes else None


def extract_dates(text):
    """
    Extract dates in various formats
    Patterns: DD/MM/YYYY or DD-MM-YYYY
    """
    date_pattern = r'\b\d{1,2}[/-]\d{1,2}[/-]\d{2,4}\b'
    dates = re.findall(date_pattern, text)
    return dates


def extract_email(text):
    """Extract email addresses"""
    email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
    emails = re.findall(email_pattern, text)
    return emails

print("✅ Pattern extraction functions created!")

How Regex Patterns Work:

Pattern: \b\d{10}\b
Explained:
\b       = Word boundary (start/end of number)
\d       = Any digit (0-9)
{10}     = Exactly 10 times
\b       = Word boundary

Example matches:
✅ 9680387400
✅ 8123456789
❌ 968038740 (only 9 digits)
❌ 96803874001 (11 digits)

Step 8: Output Formatting Functions

Create clean, professional outputs:

def format_as_text(data_dict):
    """
    Format as clean, readable text
    Perfect for printing or saving as .txt file
    """
    output = "\n" + "=" * 80 + "\n"
    output += "EXTRACTED FORM DATA\n"
    output += "=" * 80 + "\n\n"
    
    for key, value in data_dict.items():
        # Convert snake_case to Title Case
        display_key = key.replace('_', ' ').title()
        output += f"{display_key}: {value}\n"
    
    output += "\n" + "=" * 80 + "\n"
    return output


def format_as_json(data_dict):
    """
    Format as JSON (useful for APIs, databases, web apps)
    """
    return json.dumps(data_dict, indent=2, ensure_ascii=False)


def format_as_excel(data_dict, filename="extracted_data.xlsx"):
    """
    Save as Excel file
    Perfect for data analysis, sharing with teams
    """
    df = pd.DataFrame([data_dict])
    df.to_excel(filename, index=False)
    print(f"✅ Excel file saved: {filename}")
    return filename

print("✅ Output formatting functions created!")

Output Format Comparison:

Format	Best For	File Size	Human Readable
Text	Quick viewing, printing	Small	✅ Very
JSON	APIs, databases, web apps	Medium	✅ Moderate
Excel	Data analysis, sharing	Large	✅ Very

Step 9: Main Processing Pipeline (Single Image)

Combine everything into one complete workflow:

def process_single_image(image_path, output_formats=['text', 'json', 'excel']):
    """
    Complete pipeline for processing ONE form image
    
    Pipeline:
    1. Display image (visual confirmation)
    2. Extract all text using OCR
    3. Find specific patterns (phone, pincode, dates)
    4. Organize into dictionary
    5. Output in requested formats
    """
    
    print("\n" + "🚀 STARTING SINGLE IMAGE PROCESSING\n")
    print("=" * 80)
    
    # Step 1: Display the image
    display_image(image_path)
    
    # Step 2: Extract all text
    raw_text, structured_data = extract_text_from_image(image_path)
    
    # Step 3: Create organized data dictionary
    extracted_data = {
        'image_name': os.path.basename(image_path),
        'processing_date': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
        'total_text_elements': len(structured_data)
    }
    
    # Step 4: Extract specific information
    print("\n📋 EXTRACTING SPECIFIC INFORMATION...\n")
    
    # Phone numbers
    phones = extract_phone_numbers(raw_text)
    if phones:
        extracted_data['mobile_numbers'] = ', '.join(phones)
        print(f"📱 Mobile Numbers Found: {', '.join(phones)}")
    
    # Pincode
    pincode = extract_pincode(raw_text)
    if pincode:
        extracted_data['pincode'] = pincode
        print(f"📮 Pincode Found: {pincode}")
    
    # Dates
    dates = extract_dates(raw_text)
    if dates:
        extracted_data['dates_found'] = ', '.join(dates)
        print(f"📅 Dates Found: {', '.join(dates)}")
    
    # Emails
    emails = extract_email(raw_text)
    if emails:
        extracted_data['emails'] = ', '.join(emails)
        print(f"📧 Emails Found: {', '.join(emails)}")
    
    # Store complete raw text
    extracted_data['raw_text'] = raw_text
    
    print("\n" + "=" * 80)
    
    # Step 5: Output in requested formats
    results = {}
    
    if 'text' in output_formats:
        print("\n📄 TEXT FORMAT OUTPUT:\n")
        text_output = format_as_text(extracted_data)
        print(text_output)
        results['text'] = text_output
    
    if 'json' in output_formats:
        print("\n📦 JSON FORMAT OUTPUT:\n")
        json_output = format_as_json(extracted_data)
        print(json_output)
        results['json'] = json_output
    
    if 'excel' in output_formats:
        print("\n📊 EXCEL FORMAT OUTPUT:\n")
        excel_file = format_as_excel(extracted_data)
        results['excel'] = excel_file
    
    print("\n✅ PROCESSING COMPLETE!")
    print("=" * 80)
    
    return extracted_data, results

print("✅ Main processing pipeline created!")

Step 10: Batch Processing (Multiple Images)

Process many forms at once:

def process_batch_images(image_paths):
    """
    Process multiple form images in one go
    
    Input: List of image file paths
    Output: Combined Excel file with all forms
    """
    
    print("\n" + "🚀 STARTING BATCH PROCESSING\n")
    print(f"📁 Total images to process: {len(image_paths)}")
    print("=" * 80)
    
    all_data = []
    successful = 0
    failed = 0
    
    for idx, image_path in enumerate(image_paths, 1):
        print(f"\n🔄 Processing {idx}/{len(image_paths)}: {os.path.basename(image_path)}")
        print("-" * 80)
        
        try:
            # Extract text
            raw_text, structured_data = extract_text_from_image(image_path)
            
            # Create data dictionary
            data = {
                'form_number': idx,
                'image_name': os.path.basename(image_path),
                'mobile_numbers': ', '.join(extract_phone_numbers(raw_text)),
                'pincode': extract_pincode(raw_text),
                'dates_found': ', '.join(extract_dates(raw_text)),
                'emails': ', '.join(extract_email(raw_text)),
                'total_text_elements': len(structured_data),
                'raw_text': raw_text[:500]  # First 500 chars only for Excel
            }
            
            all_data.append(data)
            successful += 1
            print(f"✅ Success: {os.path.basename(image_path)}")
            
        except Exception as e:
            failed += 1
            print(f"❌ Error: {str(e)}")
            continue
    
    # Create combined Excel file
    print("\n" + "=" * 80)
    print("📊 Creating combined Excel file...")
    
    df = pd.DataFrame(all_data)
    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
    output_filename = f"batch_extracted_{timestamp}.xlsx"
    df.to_excel(output_filename, index=False)
    
    print(f"\n✅ BATCH PROCESSING COMPLETE!")
    print(f"📁 Output File: {output_filename}")
    print(f"✅ Successful: {successful}")
    print(f"❌ Failed: {failed}")
    print("=" * 80)
    
    return df, output_filename

print("✅ Batch processing function created!")

Step 11: User Interface Functions

Easy upload and processing:

def run_single_image_mode():
    """
    User-friendly function to upload and process ONE image
    """
    print("\n" + "📤 SINGLE IMAGE MODE\n")
    print("Please upload your form image (JPG, PNG, JPEG)")
    print("=" * 80 + "\n")
    
    # Upload file in Google Colab
    uploaded = files.upload()
    
    if uploaded:
        # Get uploaded filename
        image_path = list(uploaded.keys())[0]
        
        # Process the image
        extracted_data, results = process_single_image(
            image_path, 
            output_formats=['text', 'json', 'excel']
        )
        
        print("\n💾 FILES READY FOR DOWNLOAD:")
        print("1. Check the 'Files' panel on the left")
        print("2. Right-click on 'extracted_data.xlsx' → Download")
        
        return extracted_data, results
    else:
        print("❌ No file uploaded!")
        return None, None


def run_batch_mode():
    """
    User-friendly function to process MULTIPLE images
    Upload a ZIP file containing all form images
    """
    print("\n" + "📤 BATCH PROCESSING MODE\n")
    print("Please upload a ZIP file containing multiple form images")
    print("=" * 80 + "\n")
    
    # Upload ZIP file
    uploaded = files.upload()
    
    if uploaded:
        zip_path = list(uploaded.keys())[0]
        
        # Extract ZIP file
        import zipfile
        extract_dir = "extracted_forms"
        os.makedirs(extract_dir, exist_ok=True)
        
        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
            zip_ref.extractall(extract_dir)
        
        # Find all image files
        image_extensions = ['.jpg', '.jpeg', '.png', '.JPG', '.JPEG', '.PNG']
        image_paths = []
        
        for root, dirs, files_list in os.walk(extract_dir):
            for file in files_list:
                if any(file.endswith(ext) for ext in image_extensions):
                    image_paths.append(os.path.join(root, file))
        
        print(f"✅ Found {len(image_paths)} images in ZIP file\n")
        
        # Process all images
        df, output_file = process_batch_images(image_paths)
        
        print("\n💾 DOWNLOAD YOUR RESULTS:")
        print(f"File: {output_file}")
        print("Location: Files panel (left sidebar)")
        
        return df, output_file
    else:
        print("❌ No file uploaded!")
        return None, None

print("✅ User interface functions created!")

🎮 How to Use Your OCR System

For Single Image:

# Run this cell to process ONE form
data, results = run_single_image_mode()

What happens:

Upload dialog appears
Select your form image
Wait 30-60 seconds
See results in 3 formats!
Download Excel file from left panel

For Multiple Images (Batch):

# Run this cell to process MANY forms at once
df, output_file = run_batch_mode()

What happens:

Upload dialog appears
Select your ZIP file (containing multiple form images)
System processes all forms automatically
Creates ONE combined Excel file
Download results!

📊 Sample Output

Example Form Input:

[CSC Student Information Form Image]
Name: R.ABDUL RAHEEM
DOB: 02/12/2010
Mobile: 9680387400
Address: No:3/A Kanmathan Kail...

Text Output:

================================================================================
EXTRACTED FORM DATA
================================================================================

Image Name: student_form.jpg
Processing Date: 2025-11-29 15:30:00
Total Text Elements: 42
Mobile Numbers: 9680387400, 9680387400
Pincode: 600043
Dates Found: 02/12/2010, 08/11/2023
Raw Text: R.ABDUL RAHEEM
02/12/2010
Asfirgan Anna Centennial High School
P.RITZWAN
...

================================================================================

JSON Output:

{
  "image_name": "student_form.jpg",
  "processing_date": "2025-11-29 15:30:00",
  "total_text_elements": 42,
  "mobile_numbers": "9680387400, 9680387400",
  "pincode": "600043",
  "dates_found": "02/12/2010, 08/11/2023",
  "raw_text": "R.ABDUL RAHEEM\n02/12/2010\n..."
}

Excel Output:

Image Name	Mobile Numbers	Pincode	Dates Found	Raw Text
form1.jpg	9680387400	600043	02/12/2010	R.ABDUL…

🎯 Accuracy & Performance

Expected Accuracy:

Text Type	Accuracy	Notes
Printed (English)	95-98%	Very reliable
Printed (Tamil)	90-95%	Good, may need review
Handwritten (Clear)	85-92%	Depends on writing quality
Handwritten (Messy)	70-85%	May need manual verification

Processing Time:

Task	Time	Notes
Setup (First time)	3-5 min	Library installation
Single image	30-60 sec	Depends on image size
Batch (10 forms)	5-10 min	Parallel processing
Batch (50 forms)	20-30 min	Worth the automation!

💡 Tips for Better Results

Image Quality Tips:

Good Lighting:
- ✅ Natural daylight or bright white light
- ❌ Avoid yellow/dim lighting
- ❌ Avoid shadows on form
Camera Position:
- ✅ Hold phone directly above form (90° angle)
- ✅ Fill entire frame with form
- ❌ Avoid angled/tilted shots
Form Condition:
- ✅ Flat, not bent or folded
- ✅ Clean (no coffee stains, tears)
- ✅ High contrast (dark pen on white paper)
Resolution:
- ✅ Minimum 1000×1000 pixels
- ✅ Higher resolution = better accuracy
- ❌ Don’t over-compress (avoid heavy JPEG compression)

Handwriting Tips:

For Students Filling Forms:
- Use BLOCK LETTERS (not cursive)
- Write larger than usual
- Use dark pen (avoid pencil)
- Stay within boxes/lines
For Scanning:
- Scan at 300 DPI or higher
- Use grayscale mode
- Adjust brightness/contrast if needed

🐛 Troubleshooting Common Issues

Issue 1: “No module named ‘easyocr'”

Solution:

# Re-run installation cell
!pip install easyocr

Issue 2: Low Accuracy / Wrong Text

Possible Causes:

Poor image quality (blur, low light)
Messy handwriting
Colored paper (use white forms)

Solutions:

Retake photo with better lighting
Increase image resolution
Use scanner instead of phone camera
Adjust confidence threshold: if confidence > 0.5: # Increase from 0.3 to 0.5

Issue 3: Missing Phone Numbers

Solution:

# Adjust phone number pattern to be more flexible
phone_pattern = r'\d{10}'  # Matches 10 digits anywhere

Issue 4: Slow Processing

Solutions:

Enable GPU in Colab:
- Runtime → Change runtime type → GPU
- Change gpu=False to gpu=True
Reduce image size: # Add before processing img = cv2.resize(img, (0,0), fx=0.5, fy=0.5)

Issue 5: Tamil Text Not Recognized

Solution:

# Verify Tamil model is loaded
reader = easyocr.Reader(['en', 'ta'], gpu=False)

# Check installed languages
print(reader.lang_list)  # Should show ['en', 'ta']

Building an OCR Form Data Extractor: From Zero to Hero

📌 Project Overview

🎯 What We’re Solving

🛠️ Tech Stack

📚 Understanding the Fundamentals

What is OCR (Optical Character Recognition)?

Why Handwriting Recognition is Harder

Understanding Confidence Scores

🚀 Project Setup

Step 1: Open Google Colab

Step 2: Install Required Libraries

📦 Building the Project: Step-by-Step

Step 3: Import Libraries

Step 4: Initialize OCR Reader

Step 5: Image Preprocessing Functions

Step 6: Text Extraction Function (The Core!)

Step 7: Smart Pattern Extraction

Step 8: Output Formatting Functions

Step 9: Main Processing Pipeline (Single Image)

Step 10: Batch Processing (Multiple Images)

Step 11: User Interface Functions

🎮 How to Use Your OCR System

For Single Image:

For Multiple Images (Batch):

📊 Sample Output

Example Form Input:

Text Output:

JSON Output:

Excel Output:

🎯 Accuracy & Performance

Expected Accuracy:

Processing Time:

💡 Tips for Better Results

Image Quality Tips:

Handwriting Tips:

🐛 Troubleshooting Common Issues

Issue 1: “No module named ‘easyocr'”

Issue 2: Low Accuracy / Wrong Text

Issue 3: Missing Phone Numbers

Issue 4: Slow Processing

Issue 5: Tamil Text Not Recognized

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