How to Build an Image-to-Text OCR Python App With Streamlit

A Step-by-Step Tutorial on Leveraging OCR Options (Tesseract, EasyOCR, and GPT-4 Vision) and Optimizing for SEO

Name: Tison Brokenshire

Updated on 4/2/2025

A Step-by-Step Tutorial on Leveraging OCR Options (Tesseract, EasyOCR, and GPT-4 Vision) and Optimizing for SEO

Introduction

Optical Character Recognition (OCR) enables you to extract readable text from images, PDFs, or handwritten notes, making it one of the most in-demand computer vision tasks today. Whether it’s digitizing documents, scanning receipts, or turning screenshots into editable text, OCR has a wide range of applications.

In this tutorial, we’ll walk you through how to build a simple image-to-text OCR app in Python using Streamlit. We’ll also discuss multiple OCR options including Tesseract (opens in a new tab), EasyOCR (opens in a new tab), and emerging LLM-based OCR solutions like GPT-4 Vision.

Feel free to jump to any relevant section:

1. Project Overview

Our final application will allow a user to:

Upload an image file (such as a .png, .jpg, or .jpeg).
Perform OCR on the uploaded image.
Display the extracted text.

streamlit ocr app

We’ll use Streamlit to build a simple and interactive web-based interface. The core OCR functionality can be supplied by Tesseract, EasyOCR, or another advanced approach like GPT-4 Vision (if and when it’s accessible to you).

2. Setting Up Your Environment

Before we dive in, let’s install the needed libraries in a fresh environment.

Prerequisites

Python 3.7 or above
Pip or Conda for package management

Installation Steps

# Create a new virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
 
# Install Streamlit
pip install streamlit
 
# OCR libraries
pip install pytesseract  # For Tesseract
pip install easyocr       # For EasyOCR
 
# If you want to try Google Cloud Vision or other advanced APIs, install google-cloud-vision
# pip install google-cloud-vision
 
# Additional libraries
pip install pillow        # For image processing

Note: You will need to install Tesseract (opens in a new tab) on your system if you plan to use it locally. EasyOCR works out-of-the-box once installed via pip.

3. OCR Options Overview

3.1 Tesseract

Pros: Free, open-source, widely used.
Cons: Not always the best accuracy on complex images, especially with unusual fonts or lower quality images.

3.2 EasyOCR

Pros: Simple to set up, supports 80+ languages, often better with complex fonts than Tesseract.
Cons: Larger library size, can be slower for large deployments.

3.3 GPT-4 Vision or GPT4-O

Pros: State-of-the-art performance, ability to understand context, can handle complex image scenarios (like identifying text within cluttered backgrounds).
Cons: Not widely available via a public API at the time of writing; may have usage costs, and real-time or batch usage may be subject to rate limits.

4. Implementing OCR with Tesseract

Let’s first see how you might implement a simple Python function that does OCR with Tesseract.

4.1 Installing Tesseract

On Ubuntu/Debian: sudo apt-get install tesseract-ocr
On Windows: Download the installer (opens in a new tab). Remember to note the install path for pytesseract.

4.2 Sample Code (Tesseract)

import pytesseract
from PIL import Image
 
# Example path for Tesseract on Windows (adjust accordingly):
# pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
 
def ocr_with_tesseract(image_path):
    """
    Perform OCR on the given image using Tesseract.
    """
    image = Image.open(image_path)
    text = pytesseract.image_to_string(image)
    return text
 
if __name__ == "__main__":
    text_output = ocr_with_tesseract("sample_image.png")
    print(text_output)

5. Implementing OCR with EasyOCR

If Tesseract doesn’t meet your needs or you want to experiment with a different engine, try EasyOCR:

import easyocr
 
def ocr_with_easyocr(image_path, lang_list=['en']):
    """
    Perform OCR on the given image using EasyOCR.
    """
    reader = easyocr.Reader(lang_list)  # Initialize with a list of languages
    result = reader.readtext(image_path)
    # EasyOCR returns bounding boxes and text, so let's just combine the text parts
    extracted_text = "\n".join([res[1] for res in result])
    return extracted_text
 
if __name__ == "__main__":
    text_output = ocr_with_easyocr("sample_image.png")
    print(text_output)

Tip: You can initialize easyocr.Reader(['en', 'fr', ...]) to work with multiple languages. However, the more languages you add, the more computing resources and time it may require.

6. Exploring GPT-4 Vision or GPT4-O

GPT-4 Vision (or GPT4-O) is an emerging approach that uses large language models capable of image understanding. While it is not universally accessible at the time of writing, here’s the conceptual approach to using LLM-based OCR:

Upload your image to a GPT-4 Vision-enabled platform.
Prompt the model with a request like: “Please read all text in this image and provide the extracted text as output.”
Receive the text output (and possibly additional contextual insights).

If or when GPT-4 Vision becomes publicly available via an API, your code might look like this (hypothetically):

import requests
 
def ocr_with_gpt4_vision(image_path):
    """
    Hypothetical usage - Example only. 
    GPT-4 Vision is currently not widely available via a direct API.
    """
    with open(image_path, 'rb') as f:
        image_data = f.read()
 
    # Example: sending request to GPT-4 Vision endpoint
    url = "https://api.openai.com/v1/images/ocr"
    headers = {"Authorization": "Bearer YOUR_API_KEY"}
    files = {"image_file": image_data}
 
    response = requests.post(url, headers=headers, files=files)
 
    # This is purely hypothetical
    if response.status_code == 200:
        return response.json()["text"]
    else:
        raise Exception(f"Error: {response.text}")

Note: The above code is a placeholder for demonstration, as GPT-4 Vision does not offer a widely-available direct REST API at the time of writing.

7. Building the Streamlit App

Now we’ll build a Streamlit app that ties it all together. The basic workflow:

User uploads image via Streamlit’s file_uploader.
Select OCR engine (Tesseract or EasyOCR) from a dropdown or radio button.
Run OCR on the uploaded image.
Display the extracted text.

7.1 Streamlit App Code

Create a file named app.py (or any other name you prefer):

import streamlit as st
from PIL import Image
import pytesseract
import easyocr
 
# If needed, set the path to Tesseract (on Windows)
# pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
 
def ocr_with_tesseract(image):
    text = pytesseract.image_to_string(image)
    return text
 
def ocr_with_easyocr(image, lang_list=['en']):
    reader = easyocr.Reader(lang_list)
    # Streamlit's uploaded file is in-memory, so we can just pass that to PIL
    # But EasyOCR expects a file path OR raw data
    result = reader.readtext(image, detail=0)  
    # detail=0 returns just the text
    return "\n".join(result)
 
def main():
    st.title("Image to Text OCR App")
    
    st.write("Select an OCR engine:")
    ocr_engine = st.radio("OCR Engine", ("Tesseract", "EasyOCR"))
    
    uploaded_file = st.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"])
    
    if uploaded_file is not None:
        image = Image.open(uploaded_file)
        st.image(image, caption="Uploaded Image", use_column_width=True)
        
        if st.button("Extract Text"):
            with st.spinner("Performing OCR..."):
                if ocr_engine == "Tesseract":
                    extracted_text = ocr_with_tesseract(image)
                else:
                    extracted_text = ocr_with_easyocr(uploaded_file, lang_list=['en'])
                
            st.success("OCR Complete!")
            st.text_area("Extracted Text", value=extracted_text, height=200)
 
if __name__ == "__main__":
    main()

7.2 Running the App

streamlit run app.py

Point your browser to the local URL that appears (usually http://localhost:8501 (opens in a new tab)).

8. Deploying Your App & SEO Tips

8.1 Deployment Options

Streamlit Cloud: Easiest way; free tier allows you to host small apps.
Heroku: Container-based platform; you’d need a requirements.txt and a Procfile.
AWS, Azure, GCP: For larger-scale deployments. You can use Docker containers or directly push to services like AWS Elastic Beanstalk or Google App Engine.

8.2 SEO Tips for Your OCR Tutorial/App

Include relevant keywords: Terms like “OCR app in Python,” “Streamlit OCR tutorial,” “Image to text tool,” “Tesseract vs EasyOCR”.
Write descriptive headers and subheaders: Search engines give weight to headings (H1, H2, H3). We used them here to structure the content.
Add images and alt tags: Show screenshots of your app in action. Use alt tags for accessibility and SEO.
Provide code snippets and explanations: Technical tutorials often rank higher when well-structured with code.
Link to external, authoritative sources: For example, Tesseract docs, EasyOCR GitHub, official GPT-4 documentation, etc.
Use a friendly, shareable URL: e.g., mysite.com/blog/python-ocr-streamlit-tutorial.
Frequently update: Keep your tutorial current with new OCR engines, GPT-4 Vision updates, and Streamlit features.

9. Conclusion

You now have a fully functional Image-to-Text OCR application in Python using Streamlit. We’ve also explored multiple OCR solutions—from classical open-source Tesseract to EasyOCR and even the potential of LLM-based OCR like GPT-4 Vision.

Feel free to customize the interface, enhance language detection, or integrate advanced ML-based OCR solutions. By following the SEO tips above, you can also ensure that your tutorial or project page is discoverable by developers and hobbyists alike.

Happy Coding!

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