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from keras.datasets import mnist
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import matplotlib.pyplot as plt
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import cv2
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import numpy as np
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from keras.models import Sequential
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from keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout
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from keras.optimizers import SGD, Adam
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from keras.callbacks import ReduceLROnPlateau, EarlyStopping
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from keras.utils import to_categorical
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import pandas as pd
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import numpy as np
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from sklearn.model_selection import train_test_split
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from keras.utils import np_utils
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import matplotlib.pyplot as plt
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from tqdm import tqdm_notebook
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from sklearn.utils import shuffle
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# Read the data...
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data = pd.read_csv(r"E:\VSCODE19115065円\handwritten-character-recognition-code\code-files\A_Z Handwritten Data.csv").astype('float32')
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# Split data the X - Our data , and y - the prdict label
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X = data.drop('0',axis = 1)
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y = data['0']
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# Reshaping the data in csv file so that it can be displayed as an image...
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train_x, test_x, train_y, test_y = train_test_split(X, y, test_size = 0.2)
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train_x = np.reshape(train_x.values, (train_x.shape[0], 28,28))
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test_x = np.reshape(test_x.values, (test_x.shape[0], 28,28))
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print("Train data shape: ", train_x.shape)
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print("Test data shape: ", test_x.shape)
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# Dictionary for getting characters from index values...
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word_dict = {0:'A',1:'B',2:'C',3:'D',4:'E',5:'F',6:'G',7:'H',8:'I',9:'J',10:'K',11:'L',12:'M',13:'N',14:'O',15:'P',16:'Q',17:'R',18:'S',19:'T',20:'U',21:'V',22:'W',23:'X', 24:'Y',25:'Z'}
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# Plotting the number of alphabets in the dataset...
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train_yint = np.int0(y)
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count = np.zeros(26, dtype='int')
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for i in train_yint:
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count[i] +=1
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alphabets = []
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for i in word_dict.values():
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alphabets.append(i)
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fig, ax = plt.subplots(1,1, figsize=(10,10))
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ax.barh(alphabets, count)
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plt.xlabel("Number of elements ")
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plt.ylabel("Alphabets")
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plt.grid()
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plt.show()
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#Shuffling the data ...
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shuff = shuffle(train_x[:100])
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fig, ax = plt.subplots(3,3, figsize = (10,10))
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axes = ax.flatten()
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for i in range(9):
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axes[i].imshow(np.reshape(shuff[i], (28,28)), cmap="Greys")
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plt.show()
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#Reshaping the training & test dataset so that it can be put in the model...
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train_X = train_x.reshape(train_x.shape[0],train_x.shape[1],train_x.shape[2],1)
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print("New shape of train data: ", train_X.shape)
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test_X = test_x.reshape(test_x.shape[0], test_x.shape[1], test_x.shape[2],1)
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print("New shape of train data: ", test_X.shape)
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# Converting the labels to categorical values...
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train_yOHE = to_categorical(train_y, num_classes = 26, dtype='int')
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print("New shape of train labels: ", train_yOHE.shape)
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test_yOHE = to_categorical(test_y, num_classes = 26, dtype='int')
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print("New shape of test labels: ", test_yOHE.shape)
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# CNN model...
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model = Sequential()
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model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)))
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model.add(MaxPool2D(pool_size=(2, 2), strides=2))
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model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding = 'same'))
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model.add(MaxPool2D(pool_size=(2, 2), strides=2))
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model.add(Conv2D(filters=128, kernel_size=(3, 3), activation='relu', padding = 'valid'))
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model.add(MaxPool2D(pool_size=(2, 2), strides=2))
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model.add(Flatten())
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model.add(Dense(64,activation ="relu"))
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model.add(Dense(128,activation ="relu"))
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model.add(Dense(26,activation ="softmax"))
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model.compile(optimizer = Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
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reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=1, min_lr=0.0001)
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early_stop = EarlyStopping(monitor='val_loss', min_delta=0, patience=2, verbose=0, mode='auto')
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history = model.fit(train_X, train_yOHE, epochs=1, callbacks=[reduce_lr, early_stop], validation_data = (test_X,test_yOHE))
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model.summary()
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model.save(r'model_hand.h5')
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# Displaying the accuracies & losses for train & validation set...
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print("The validation accuracy is :", history.history['val_accuracy'])
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print("The training accuracy is :", history.history['accuracy'])
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print("The validation loss is :", history.history['val_loss'])
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print("The training loss is :", history.history['loss'])
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#Making model predictions...
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pred = model.predict(test_X[:9])
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print(test_X.shape)
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# Displaying some of the test images & their predicted labels...
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fig, axes = plt.subplots(3,3, figsize=(8,9))
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axes = axes.flatten()
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for i,ax in enumerate(axes):
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img = np.reshape(test_X[i], (28,28))
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ax.imshow(img, cmap="Greys")
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pred = word_dict[np.argmax(test_yOHE[i])]
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ax.set_title("Prediction: "+pred)
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ax.grid()
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# Prediction on external image...
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img = cv2.imread(r'E:\VSCODE19115065円\handwritten-character-recognition-code\code-files\image\img-m.jpg')
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img_copy = img.copy()
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = cv2.resize(img, (400,440))
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img_copy = cv2.GaussianBlur(img_copy, (7,7), 0)
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img_gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
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_, img_thresh = cv2.threshold(img_gray, 100, 255, cv2.THRESH_BINARY_INV)
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img_final = cv2.resize(img_thresh, (28,28))
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img_final =np.reshape(img_final, (1,28,28,1))
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img_pred = word_dict[np.argmax(model.predict(img_final))]
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cv2.putText(img, "Prediction: " + img_pred, (20,410), cv2.FONT_HERSHEY_DUPLEX, 1.3, color = (255,0,30))
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cv2.imshow('handwritten character recognition _ _ _ ', img)
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while (1):
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k = cv2.waitKey(1) & 0xFF
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if k == 27:
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break
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cv2.destroyAllWindows()
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# Handwritten-Character-Recognition
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This is a project for handwritten character recognition using neural network.
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For the dataset of this project visit - https://www.kaggle.com/datasets/sachinpatel21/az-handwritten-alphabets-in-csv-format?resource=download
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# Handwritten-Character-Recognition
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It takes image of human written character and identifies the character as an output.
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This project has been tested over thousands of dataset, and passed with an accuracy of over 80 percent.
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## Setup instructions
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Python version used: Python 3.10.7
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For the dataset of this project visit - https://www.kaggle.com/datasets/sachinpatel21/az-handwritten-alphabets-in-csv-format?resource=download
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## Output
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Display images/gifs/videos of output/result of your script so that users can visualize it
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## Author(s)
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Mukesh751
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