Handwritten Equation Solver in Python

File handling | NumPy | Python functions

Michael Zippo 18.07.2021

Retrieving training data

Loading dataset

link

Feature Extraction

Invert the image and then convert it to binary image because extracting contours is best when the object is white and the environment is black.
Use findContour to find contours. For objects, get the bounding rectangle-method/">rectangle of the path using the boundingRect function (the bounding rectangle-method/">rectangle — is the smallest horizontal rectangle-method/">rectangle that encloses the entire path).
Since each image in our dataset contains only one character / digit, we only the bounding rectangle-method/">rectangle of the maximum size is needed. To do this, we calculate the area of the bounding rectangle-method/">rectangle of each path and select the rectangle-method/">rectangle with the maximum area.
Now change the maximum size of the bounding rectangle-method/">rectangle to 28 by 28. Change it to 784 by 1. This will now have 784 pixel values or function. Now assign a label to it (for example, for 0-9 images, the same label as their digit, for — assign a label 10, for + assign a label 11, for a time stamp, assign a label 12). So now our dataset contains 784 feature columns and one label column. After extracting the functions, save the data to a CSV file.

Train the data using a convolutional neural network

Since a convolutional neural network operates on 2D data, and our dataset has a shape of 785 to 1. So we need to change it. First, assign the y_train variable to the label column in our dataset. Then drop the label column from the dataset and then change it to 28 to 28. Our dataset is now ready for CNN.

Building a convolutional neural network

To create a CNN, import all required libraries.

Convert y_train data to categorical data using the to_categorical function. Use the following line of code to create the model.

import pandas as pd

import numpy as np

import pickle

np.random.seed ( 1212 )

import keras

from keras.models import Model

from keras.layers < / code> import * from keras import optimizers

   from   keras.layers   import   Input   , Dense 
   from   keras.models   import   Sequential 
   from   keras.layers   import   Dense 
   from   keras.layers   import   Dropout 
   from   keras.layers   import   Flat ten 
   from   keras.layers.convolutional   import   Conv2D 
   from   keras.layers. convolutional   import   MaxPooling2D 
   from   keras.utils   import   np_utils 
   from   keras   import   backend as K  
   K.set_image_dim_ordering (  ’th’  ) 
   from   keras.utils.np_utils   import   to_categorical 
   from   ker as.models   import   model_from_json

model = Sequential ()

model.add (Conv2D ( 30 , ( 5 , 5 ), input_shape = ( 1 , 28 , 28 ), activation = ’relu’ ))

model.add (MaxPooling2D (pool_size = ( 2 , 2 )))

model.add (Conv2D ( 15 , ( 3 , 3 ), activation = ’ relu’ ))

model.add (MaxPooling2D (pool_size = ( 2 , 2 )))

model.add (Dropout ( 0.2 ))

model.add (Flatten ())

model.add (Dense ( 128 , activation = ’relu’ ))

   model.add (Dense (  50  , activation   =   ’relu’  )) 
   model.add (Dense (  13  , activation   =   ’softmax’  )) 
   # Compile the model  
   model.   compile   (loss   =   ’categorical_crossentropy’  , 
   optimizer   =   ’adam’  , metrics   =   [  ’accuracy’  ])

Fitting the model to the data

Use the following lines of code to fit the CNN to the data.

       model.fit (np.array (l), cat, epochs   =   10  , batch_size   =   200  ,  
   shuffle   =   True  , verbose   =   1  )

Training our model will take about three hours with an accuracy of 98.46%. After training, we can save our model as a json file for future use so that we don’t have to train our model and wait three hours each time. To save our model, we can use the following line of codes.

model_json = model.to_json ()

with open ( "model_final.json" , " w " ) as json_file:

json_file.write (model_json)

# serialize weights to HDF5

model .save_weights ( "model_final.h5" )

Testing our model or solving an equation with it

First, import to shu the saved model using the following line of codes.

json_file = open ( ’ model_final.json’ , ’r’ )

loaded_model_json = json_file.read ()

json_file.close ()

loaded_model = model_from_json (loaded_model_json)

# load weight into new model

loaded_model.load_weights ( "model_final.h5" )

Now enter an image containing a handwritten equation. Convert the image to binary and then invert the image (if numbers / characters are in black).

Now we get the outlines of the image, by default we get the outlines from left to right.

Get the bounding rectangle-method/">rectangle for each outline.

This sometimes results in two or more outlines for the same digit / character. To avoid this, check if the bounding rectangle-method/">rectangle overlaps these two paths or not. If they overlap, then drop the smaller rectangle-method/">rectangle.

Now resize the entire remaining bounding rectangle-method/">rectangle from 28 to 28.

Using the model, predict the corresponding digit / symbol for each bounding rectangle-method/">rectangle and save it as a string.

Then use the & # 39; eval & # 39; in the line to solve the equation.

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Handwritten Equation Solver in Python

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