I am working in a project named "Handwritten Math Evaluation". So what basically happens in this is that there are 11 classes of (0 - 9) and (+, -) each containing 50 clean handwritten digits in them. Then I trained a CNN model for it with 80 % of data used in training and 20 % using in testing of model which results in an accuracy of 98.83 %. Here is the code for the architecture of CNN 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 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 Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
from keras.utils.np_utils import to_categorical
from keras.models import model_from_json
import matplotlib.pyplot as plt
model = Sequential()
model.add(Conv2D(30, (5, 5), input_shape =(28,28,1), 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(12, activation ='softmax'))
# Compile model
model.compile(loss ='categorical_crossentropy',
optimizer ='adam', metrics =['accuracy'])
model.fit(X_train, y_train, epochs=1000)
Now each image in dataset is preprocessed as follows:
import cv2
im = cv2.imread(path)
im_gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
ret, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV)
ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
rects = [cv2.boundingRect(ctr) for ctr in ctrs]
rect = rects[0]
im_crop =im_th[rect[1]:rect[1]+rect[3],rect[0]:rect[0]+rect[2]]
im_resize = cv2.resize(im_crop,(28,28))
im_resize = np.array(im_resize)
im_resize=im_resize.reshape(28,28)
I have made an evaluation function which solves simple expression like 7+8 :-
def evaluate(im):
s = ''
data = []
im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
ret, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV)
ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
boundingBoxes = [cv2.boundingRect(c) for c in ctrs]
look_up = ['0','1','2','3','4','5','6','7','8','9','+','-']
i=0
for c in ctrs:
rect = boundingBoxes[i]
im_crop = im_th[rect[1]:rect[1]+rect[3], rect[0]:rect[0]+rect[2]]
im_resize = cv2.resize(im_crop,(28,28))
im_resize = np.array(im_resize)
im_resize = im_resize.reshape(28,28,1)
data.append(im_resize)
i+=1
data = np.array(data)
predictions = model.predict(data)
i=0
while i<len(boundingBoxes):
rect = boundingBoxes[i]
print(rect[2],rect[3])
print(predictions[i])
s += look_up[predictions[i].argmax()]
i+=1
return s
I need help extending this, thought for compound fractions but the problem is that they are identical to subtraction sign when resized to (28, 28). So I need help in distinguishing between them.
This is my first question, so please let me know if any details are left.
