Simple python project for evaluation of handwritten mathematical expressions. Supports digits 0-9 and operators for addition, subtraction, multiplication, division, and brackets:

0123456789+-×/()

main.py takes one argument, -i or --image, path to image of handwritten expression. The image is processed and passed to optical character detector which cuts out individual characters and sends them to a simple CNN classifier. Expression constructed from classified characters is passed to an evaluator utilizing Shunting-Yard algorithm. Solution is printed to standard output.

Installation is possible with docker or manually. For Docker install within this directory use:

docker build --tag ocr-math-solver .    
docker run ocr-math-solver -i ./expression_examples/expression3.jpg

to install without Docker use:

pip3 install -r requirements.txt

python main.py --image | -i PATH_TO_IMAGE

for example:

python main.py -i ./expression_examples/expression3.jpg

Input expression:

After preprocessing and character detection:

Output:

3 + 45 x ( 6 + 1 )
= 318

The network definition can be found in classifier/model.py. Pre-trained model model.h5 was trained on a subset of CROHME offline handwritten dataset. dataset.read_images() can be used to read this dataset and prepare it as a numpy array. To begin training the network, simply run:

python ./classifier/model.py