Handwritten Equation Solver – IT and Computer Engineering Guide
1. Project Overview
Objective: Develop a system that recognizes handwritten
mathematical equations from images and solves them.
Scope: Use Convolutional Neural Networks (CNNs) and Optical Character
Recognition (OCR) techniques to extract expressions from images and compute
solutions.
2. Prerequisites
Knowledge: Basics of Python programming, computer vision,
deep learning, and mathematical computation.
Tools: Python, TensorFlow/Keras, OpenCV, Tesseract OCR, and NumPy.
Dataset: Handwritten equation images (can be created or sourced from datasets
like CROHME).
3. Project Workflow
- Dataset Preparation: Collect or create a dataset of handwritten equations with annotations.
- Preprocessing: Process images to enhance quality, normalize dimensions, and binarize text.
- Character Recognition Model: Train a CNN-based OCR model to identify digits, operators, and symbols.
- Equation Parsing: Assemble recognized characters into mathematical expressions.
- Solution Computation: Evaluate the parsed expressions using Python’s math libraries.
- Model Evaluation: Assess accuracy and robustness against variations in handwriting.
- Deployment: Develop an application to take input images, output equations, and provide solutions.
4. Technical Implementation
Step 1: Import Libraries
import cv2
import numpy as np
from tensorflow.keras.models import load_model
import pytesseract
Step 2: Preprocess Images
def preprocess_image(image_path):
image = cv2.imread(image_path,
cv2.IMREAD_GRAYSCALE)
image = cv2.resize(image, (128,
32)) # Resize to consistent dimensions
_, image = cv2.threshold(image, 128,
255, cv2.THRESH_BINARY_INV) # Binarize
return image
# Example usage
image = preprocess_image('equation.jpg')
cv2.imshow('Preprocessed Image', image)
cv2.waitKey(0)
Step 3: Recognize Characters
# Load pretrained OCR model
ocr_model = load_model('ocr_model.h5')
def recognize_characters(image):
# Extract regions of interest (e.g.,
bounding boxes of characters)
# Use a model like Tesseract or a
custom-trained CNN to recognize characters
characters =
pytesseract.image_to_string(image, config='--psm 7')
return characters
# Example
expression = recognize_characters(image)
print("Recognized Expression:", expression)
Step 4: Solve the Equation
def solve_equation(expression):
try:
result = eval(expression) # Evaluate mathematical expression
return result
except Exception as e:
print("Error solving
equation:", e)
return None
# Example
solution = solve_equation(expression)
print("Solution:", solution)
Step 5: Integrate into Application
# Develop a Flask app to process images and return solutions
from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route('/solve', methods=['POST'])
def solve():
file = request.files['image']
image = preprocess_image(file)
expression =
recognize_characters(image)
solution = solve_equation(expression)
return jsonify({'expression':
expression, 'solution': solution})
if __name__ == '__main__':
app.run(debug=True)
5. Results and Insights
Evaluate the system's performance on diverse handwriting styles and assess its accuracy in solving equations.
6. Challenges and Mitigation
Handwriting Variability: Train the model on diverse
handwriting styles to improve generalization.
Complex Equations: Enhance parsing logic to handle nested or multi-line
equations.
7. Future Enhancements
Incorporate advanced sequence models like Transformer-based
OCR for better recognition.
Support for additional mathematical symbols and equations in multiple
languages.
8. Conclusion
The Handwritten Equation Solver project demonstrates the integration of computer vision and deep learning to address a challenging real-world problem, paving the way for innovative educational tools.