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Ref:

Self made hand digits and its training+prediction using Keras Conv2D model

from keras.models import Sequential
from keras.layers import Dense,Conv2D,MaxPooling2D,Flatten,Dropout
from keras.optimizers import Adam
from keras.losses import categorical_crossentropy
from keras.callbacks import TensorBoard
from keras.utils import plot_model
from keras.utils import to_categorical
import cv2 as cv
import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from keras.utils import to_categorical
from sklearn.metrics import confusion_matrix,accuracy_score

from keras import backend as K # required to clear the graph session, if any execution was done before
Using TensorFlow backend.

Read the handwritten digits and visualize them

digit_dir = "./Hand_Digits/"
cnt = 1
for dir in os.walk(digit_dir):
    for folder in dir[1]:
        for file in os.walk(digit_dir+folder):
            full_path = digit_dir+folder+"/"+file[-1][0]
#             print(full_path)
            im = cv.imread(full_path)
            plt.subplot(2,5,cnt)
            plt.imshow(im)
            plt.title(file[-1][0])
            plt.axis("off")
            cnt +=1
plt.show()            
            
Notebook Image

Image augmentation

using augmentation (ImageDataGenerator() in Keras), we will increase the image dataset by chnaging some standrad parameters