Handwritten Text Recognition

Snap a picture of handwritten text, and we'll turn it into clean, readable text for you—quick and easy!

Comment

Handwritten Text Recognition with AI

Inspiration

Reading handwritten text accurately is a common challenge, especially when dealing with different handwriting styles. We wanted to create a tool that makes it easy to extract and refine handwritten text using AI. Our goal was to help students, professionals, and researchers by automating the process with deep learning.

What It Does

  • Allows users to upload a sentence of handwritten text.
  • Uses AI-powered OCR (TrOCR) to extract text from the image.
  • Applies preprocessing techniques to enhance OCR accuracy.
  • Automatically corrects spelling mistakes using TextBlob.
  • Provides a Streamlit-based UI for ease of use.

🏗️ How We Built It

  1. Model Selection: We used TrOCR (Transformer-based OCR) for handwritten text recognition.
  2. Preprocessing: Converted images to grayscale, applied Gaussian blur and adaptive thresholding to improve text clarity.
  3. Text Correction: Integrated TextBlob for minor spelling corrections.
  4. Frontend: Built an interactive Streamlit web app for easy user interaction.

🛠️ Challenges We Ran Into

  • Handwriting varies significantly, making OCR accuracy inconsistent.
  • Some extracted text contained errors due to poor image quality or model limitations.
  • Finding the right preprocessing techniques to improve recognition results.

🎉 Accomplishments That We're Proud Of

  • Successfully integrated TrOCR for accurate handwritten text recognition.
  • Improved text accuracy through adaptive preprocessing techniques.
  • Created a fully functional and user-friendly web app with Streamlit.

📚 What We Learned

  • The impact of image preprocessing on OCR performance.
  • How to fine-tune AI models for better accuracy.
  • The importance of UX/UI design in AI-powered applications.

🔮 What's Next for Handwritten Text Recognition

  • Improving support for different handwriting styles and languages.
  • Adding a real-time handwriting recognition feature using a webcam.
  • Exploring fine-tuning TrOCR with a custom dataset for better accuracy.
  • Deploying the application as a web service for broader accessibility.

Built With

Share this project:

Updates