Merge pull request #1984 from Mukesh751/master

Added HandWritten Character Recognition Script

Author: avinashkranjan

Handwritten-Character-Recognition/Handwritten_character_recog.py

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+from keras.datasets import mnist
+import matplotlib.pyplot as plt
+import cv2
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout
+from keras.optimizers import SGD, Adam
+from keras.callbacks import ReduceLROnPlateau, EarlyStopping
+from keras.utils import to_categorical
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from keras.utils import np_utils
+import matplotlib.pyplot as plt
+from tqdm import tqdm_notebook
+from sklearn.utils import shuffle
+
+
+
+# Read the data...
+data = pd.read_csv(r"E:\VSCODE\19115065\handwritten-character-recognition-code\code-files\A_Z Handwritten Data.csv").astype('float32')
+
+# Split data the X - Our data , and y - the prdict label
+X = data.drop('0',axis = 1)
+y = data['0']
+
+
+# Reshaping the data in csv file so that it can be displayed as an image...
+
+train_x, test_x, train_y, test_y = train_test_split(X, y, test_size = 0.2)
+train_x = np.reshape(train_x.values, (train_x.shape[0], 28,28))
+test_x = np.reshape(test_x.values, (test_x.shape[0], 28,28))
+
+
+print("Train data shape: ", train_x.shape)
+print("Test data shape: ", test_x.shape)
+
+# Dictionary for getting characters from index values...
+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'}
+
+
+# Plotting the number of alphabets in the dataset...
+
+train_yint = np.int0(y)
+count = np.zeros(26, dtype='int')
+for i in train_yint:
+    count[i] +=1
+
+alphabets = []
+for i in word_dict.values():
+    alphabets.append(i)
+
+fig, ax = plt.subplots(1,1, figsize=(10,10))
+ax.barh(alphabets, count)
+
+plt.xlabel("Number of elements ")
+plt.ylabel("Alphabets")
+plt.grid()
+plt.show()
+
+
+#Shuffling the data ...
+shuff = shuffle(train_x[:100])
+
+fig, ax = plt.subplots(3,3, figsize = (10,10))
+axes = ax.flatten()
+
+for i in range(9):
+    axes[i].imshow(np.reshape(shuff[i], (28,28)), cmap="Greys")
+plt.show()
+
+
+#Reshaping the training & test dataset so that it can be put in the model...
+
+train_X = train_x.reshape(train_x.shape[0],train_x.shape[1],train_x.shape[2],1)
+print("New shape of train data: ", train_X.shape)
+
+test_X = test_x.reshape(test_x.shape[0], test_x.shape[1], test_x.shape[2],1)
+print("New shape of train data: ", test_X.shape)
+
+
+# Converting the labels to categorical values...
+
+train_yOHE = to_categorical(train_y, num_classes = 26, dtype='int')
+print("New shape of train labels: ", train_yOHE.shape)
+
+test_yOHE = to_categorical(test_y, num_classes = 26, dtype='int')
+print("New shape of test labels: ", test_yOHE.shape)
+
+
+# CNN model...
+
+model = Sequential()
+
+model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)))
+model.add(MaxPool2D(pool_size=(2, 2), strides=2))
+
+model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding = 'same'))
+model.add(MaxPool2D(pool_size=(2, 2), strides=2))
+
+model.add(Conv2D(filters=128, kernel_size=(3, 3), activation='relu', padding = 'valid'))
+model.add(MaxPool2D(pool_size=(2, 2), strides=2))
+
+model.add(Flatten())
+
+model.add(Dense(64,activation ="relu"))
+model.add(Dense(128,activation ="relu"))
+
+model.add(Dense(26,activation ="softmax"))
+
+
+
+model.compile(optimizer = Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
+reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=1, min_lr=0.0001)
+early_stop = EarlyStopping(monitor='val_loss', min_delta=0, patience=2, verbose=0, mode='auto')
+
+
+history = model.fit(train_X, train_yOHE, epochs=1, callbacks=[reduce_lr, early_stop],  validation_data = (test_X,test_yOHE))
+
+
+model.summary()
+model.save(r'model_hand.h5')
+
+
+# Displaying the accuracies & losses for train & validation set...
+
+print("The validation accuracy is :", history.history['val_accuracy'])
+print("The training accuracy is :", history.history['accuracy'])
+print("The validation loss is :", history.history['val_loss'])
+print("The training loss is :", history.history['loss'])
+
+
+
+#Making model predictions...
+
+pred = model.predict(test_X[:9])
+print(test_X.shape)
+
+
+# Displaying some of the test images & their predicted labels...
+
+fig, axes = plt.subplots(3,3, figsize=(8,9))
+axes = axes.flatten()
+
+for i,ax in enumerate(axes):
+    img = np.reshape(test_X[i], (28,28))
+    ax.imshow(img, cmap="Greys")
+    pred = word_dict[np.argmax(test_yOHE[i])]
+    ax.set_title("Prediction: "+pred)
+    ax.grid()
+
+
+# Prediction on external image...
+
+img = cv2.imread(r'E:\VSCODE\19115065\handwritten-character-recognition-code\code-files\image\img-m.jpg')
+img_copy = img.copy()
+
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+img = cv2.resize(img, (400,440))
+
+img_copy = cv2.GaussianBlur(img_copy, (7,7), 0)
+img_gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
+_, img_thresh = cv2.threshold(img_gray, 100, 255, cv2.THRESH_BINARY_INV)
+
+img_final = cv2.resize(img_thresh, (28,28))
+img_final =np.reshape(img_final, (1,28,28,1))
+
+
+img_pred = word_dict[np.argmax(model.predict(img_final))]
+
+cv2.putText(img, "Prediction: " + img_pred, (20,410), cv2.FONT_HERSHEY_DUPLEX, 1.3, color = (255,0,30))
+cv2.imshow('handwritten character recognition _ _ _ ', img)
+
+
+while (1):
+    k = cv2.waitKey(1) & 0xFF
+    if k == 27:
+        break
+cv2.destroyAllWindows()
+

Handwritten-Character-Recognition/README.md

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+# Handwritten-Character-Recognition
+This is a project for handwritten character recognition using neural network.
+For the dataset of this project visit - https://www.kaggle.com/datasets/sachinpatel21/az-handwritten-alphabets-in-csv-format?resource=download
+
+# Handwritten-Character-Recognition
+
+It takes image of human written character and identifies the character as an output.
+This project has been tested over thousands of dataset, and passed with an accuracy of over 80 percent.
+
+## Setup instructions
+
+Python version used: Python 3.10.7
+For the dataset of this project visit - https://www.kaggle.com/datasets/sachinpatel21/az-handwritten-alphabets-in-csv-format?resource=download
+
+## Output
+![Screenshot (147)](https://github.com/Mukesh751/Amazing-Python-Scripts/assets/91366697/6b592ea9-5046-4222-8b6e-0b4703518fcd)
+
+![Screenshot (148)](https://github.com/Mukesh751/Amazing-Python-Scripts/assets/91366697/5c0a87b2-433f-41cd-aab5-06818e6fb313)
+
+## Author(s)
+
+Mukesh751
+