Recognizing Persian Digits

import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
import random
import utils
from PIL import Image
from IPython.display import display
from keras.layers import Dense
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import AveragePooling2D
from keras.layers import Flatten
from keras.models import Sequential
from keras.models import load_model
from keras.optimizers import Adam
from scipy import ndimage
from __future__ import print_function
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from six.moves.urllib.request import urlretrieve
from six.moves import cPickle as pickle
%matplotlib inline

Using TensorFlow backend.

root = './data/Digits/isolated digits/'
prediction_root = './data/Digits/segmented digits/'
binary_save_path = './binary_data/isolated digits/'
model_save_path = './models/'
num_labels= 10
image_size = 28

Contents

• Loading and Preprocessing
• Model Implementation
• Training
• Model Evaluation
• Prediction

Loading and Preprocessing Data

First we will load and normalize data, then we resize all of the images in (28,28) size and save it to binary .npy file :

data_paths = utils.maybe_save(root,binary_save_path,image_size)

./binary_data/isolated digits/0.npy already present - Skipping pickling.
./binary_data/isolated digits/1.npy already present - Skipping pickling.
./binary_data/isolated digits/2.npy already present - Skipping pickling.
./binary_data/isolated digits/3.npy already present - Skipping pickling.
./binary_data/isolated digits/4.npy already present - Skipping pickling.
./binary_data/isolated digits/5.npy already present - Skipping pickling.
./binary_data/isolated digits/6.npy already present - Skipping pickling.
./binary_data/isolated digits/7.npy already present - Skipping pickling.
./binary_data/isolated digits/8.npy already present - Skipping pickling.
./binary_data/isolated digits/9.npy already present - Skipping pickling.

Checking if data is Balanced across diffrent classes :

def balancingCheck(data_folder) :
    tmpList = None
    for i,ds in enumerate(data_folder) :
        dataset = np.load(ds)
        print("number of images in %s is equal to %s" % (ds,len(dataset)))
balancingCheck(data_paths)

number of images in ./binary_data/isolated digits/0.npy is equal to 975
number of images in ./binary_data/isolated digits/1.npy is equal to 942
number of images in ./binary_data/isolated digits/2.npy is equal to 990
number of images in ./binary_data/isolated digits/3.npy is equal to 945
number of images in ./binary_data/isolated digits/4.npy is equal to 958
number of images in ./binary_data/isolated digits/5.npy is equal to 994
number of images in ./binary_data/isolated digits/6.npy is equal to 962
number of images in ./binary_data/isolated digits/7.npy is equal to 956
number of images in ./binary_data/isolated digits/8.npy is equal to 982
number of images in ./binary_data/isolated digits/9.npy is equal to 949

As you see data is almost balanced over diffrent classes.

Merging and Creating Label for datasets :

def merge_datasets(data_paths) :    
    data = np.concatenate([np.load(path) for path in data_paths],axis=0)
    label = np.concatenate([[path[-5]]*len(np.load(path)) for i,path in enumerate(data_paths)])
    return data,label
data,labels = merge_datasets(data_paths)

We should shuffle data for cnn trainer :

def randomize(dataset, labels):
    permutation = np.random.permutation(labels.shape[0])
    shuffled_dataset = dataset[permutation,:,:]
    shuffled_labels = labels[permutation]
    return shuffled_dataset, shuffled_labels
data,labels = randomize(data,labels)

We want to make sure that data still looks good :

def visualize(dataset,labels,size = 10) :
    samples = np.random.randint(len(dataset),size=size)
    for i,sample in enumerate(samples) :
        plt.subplot(np.ceil(np.sqrt(size)),np.floor(np.sqrt(size)),1+i)
        plt.axis('off')
        plt.title(labels[sample])
        plt.imshow(dataset[sample])
visualize(data,labels,10)

def reformat(data,labels) :
    data = np.array(data).reshape((-1,image_size,image_size,1)).astype(np.float32)
    labels = (np.arange(num_labels) == labels[:,None].astype(np.float32))
    return data,labels

data,labels = reformat(data,labels)
print('shape of data : {}'.format(data.shape))
print('shape of labels : {}'.format(labels.shape))

shape of data : (9653, 28, 28, 1)
shape of labels : (9653, 10)

def train_valid_test_spliter(data,labels) :
    X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=1)
    X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1)
    return X_train,y_train,X_val,y_val,X_test,y_test

X_train,y_train,X_val,y_val,X_test,y_test = train_valid_test_spliter(data,labels)

print('train data shape: {}'.format(X_train.shape))
print('train labels shape: {}'.format(X_train.shape))
print('valid data shape : {}'.format(X_val.shape))
print('valid labels shape: {}'.format(y_val.shape))
print('test data shape : {}'.format(X_test.shape))
print('test labels shape: {}'.format(y_test.shape))

train data shape: (6177, 28, 28, 1)
train labels shape: (6177, 28, 28, 1)
valid data shape : (1545, 28, 28, 1)
valid labels shape: (1545, 10)
test data shape : (1931, 28, 28, 1)
test labels shape: (1931, 10)

Model Implementation

def conv_model(input_shape) :
    
    model = Sequential()
    model.add(Conv2D(16, 5, padding='same', activation='selu', input_shape=input_shape))
    model.add(Conv2D(16, 5, padding='same', activation='selu'))
    model.add(Conv2D(16, 5, padding='same', activation='selu'))
    model.add(Conv2D(16, 5, padding='same', activation='selu'))
    model.add(Conv2D(16, 5, padding='same', activation='selu'))
    model.add(Conv2D(16, 5, padding='same', activation='selu'))
    model.add(Conv2D(16, 5, padding='same', activation='selu'))
    model.add(Conv2D(16, 5, padding='same', activation='selu'))
    model.add(MaxPooling2D())

    model.add(Conv2D(32, 3, padding='same', activation='selu'))
    model.add(Conv2D(32, 3, padding='same', activation='selu'))
    model.add(Conv2D(32, 3, padding='same', activation='selu'))
    model.add(Conv2D(32, 3, padding='same', activation='selu'))
    model.add(Conv2D(32, 3, padding='same', activation='selu'))
    model.add(Conv2D(32, 3, padding='same', activation='selu'))
    model.add(Conv2D(32, 3, padding='same', activation='selu'))
    model.add(Conv2D(32, 3, padding='same', activation='selu'))
    model.add(MaxPooling2D())

    model.add(Conv2D(64, 3, padding='same', activation='selu'))
    model.add(Conv2D(64, 3, padding='same', activation='selu'))
    model.add(Conv2D(64, 3, padding='same', activation='selu'))
    model.add(Conv2D(64, 3, padding='same', activation='selu'))
    model.add(Conv2D(64, 3, padding='same', activation='selu'))
    model.add(Conv2D(64, 3, padding='same', activation='selu'))
    model.add(Conv2D(64, 3, padding='same', activation='selu'))
    model.add(Conv2D(64, 3, padding='same', activation='selu'))
    model.add(AveragePooling2D())

    model.add(Flatten())

    model.add(Dense(1024, activation='selu'))
    model.add(Dense(1024, activation='selu'))
    model.add(Dense(10, activation='softmax'))

    model.compile(optimizer=Adam(lr=1e-4), loss='categorical_crossentropy', metrics=['accuracy'])
    
    return model

Training

def train_model(input_shape,X_train,y_train,X_val,y_val,batch_size=64,epochs=100,model_name = 'conv_model.h5') :
    model_path = os.path.join(model_save_path,model_name)
    if os.path.exists(model_path) :
        print('model already exist in : {}'.format(model_path))
        return load_model(model_path)
    else :
        model = conv_model(input_shape)
        model.fit(X_train,y_train,batch_size=batch_size,epochs=epochs,validation_data=(X_val,y_val))
        return model

model = train_model((image_size,image_size,1),X_train,y_train,
                    X_val,y_val,64,40,'conv_model.h5')

model already exist in : ./models/conv_model.h5

model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 28, 28, 16)        416       
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 28, 28, 16)        6416      
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 28, 28, 16)        6416      
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 28, 28, 16)        6416      
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 28, 28, 16)        6416      
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 28, 28, 16)        6416      
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 28, 28, 16)        6416      
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 28, 28, 16)        6416      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 16)        0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 14, 14, 32)        4640      
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 14, 14, 32)        9248      
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 14, 14, 32)        9248      
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 14, 14, 32)        9248      
_________________________________________________________________
conv2d_13 (Conv2D)           (None, 14, 14, 32)        9248      
_________________________________________________________________
conv2d_14 (Conv2D)           (None, 14, 14, 32)        9248      
_________________________________________________________________
conv2d_15 (Conv2D)           (None, 14, 14, 32)        9248      
_________________________________________________________________
conv2d_16 (Conv2D)           (None, 14, 14, 32)        9248      
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 7, 7, 32)          0         
_________________________________________________________________
conv2d_17 (Conv2D)           (None, 7, 7, 64)          18496     
_________________________________________________________________
conv2d_18 (Conv2D)           (None, 7, 7, 64)          36928     
_________________________________________________________________
conv2d_19 (Conv2D)           (None, 7, 7, 64)          36928     
_________________________________________________________________
conv2d_20 (Conv2D)           (None, 7, 7, 64)          36928     
_________________________________________________________________
conv2d_21 (Conv2D)           (None, 7, 7, 64)          36928     
_________________________________________________________________
conv2d_22 (Conv2D)           (None, 7, 7, 64)          36928     
_________________________________________________________________
conv2d_23 (Conv2D)           (None, 7, 7, 64)          36928     
_________________________________________________________________
conv2d_24 (Conv2D)           (None, 7, 7, 64)          36928     
_________________________________________________________________
average_pooling2d_1 (Average (None, 3, 3, 64)          0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 576)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 1024)              590848    
_________________________________________________________________
dense_2 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_3 (Dense)              (None, 10)                10250     
=================================================================
Total params: 2,042,394
Trainable params: 2,042,394
Non-trainable params: 0
_________________________________________________________________

Model Evaluation

scores = model.evaluate(X_test,y_test)

1931/1931 [==============================] - 2s 1ms/step

print('Accuracy : %{}'.format(scores[1]*100))
print("Error : %{}".format(100 - scores[1]*100))

Accuracy : %99.22320041429312
Error : %0.7767995857068826

Prediction

def predict_image(prediction_root,image_size,model) :
    for i,path in enumerate(os.listdir(prediction_root)):
        photoes_path = os.path.join(prediction_root,path)
        photoes = os.listdir(photoes_path)
        photo_path = np.random.choice(photoes)
        processed_image = (np.array(Image.open(os.path.join(photoes_path, photo_path)).resize((image_size,image_size))).astype(float) - 
                            255.0 / 2) / 255.0
        photo = np.array(Image.open(os.path.join(photoes_path,photo_path)).resize((image_size,image_size))).astype(np.float)
        plt.subplot(np.ceil(np.sqrt(10)),np.floor(np.sqrt(10)),1+i)
        plt.axis('off')
        plt.title(np.argmax(model.predict(processed_image.reshape(1,image_size,image_size,1))))
        plt.imshow(photo)

predict_image(prediction_root,image_size,model)

Refrence

Sadri, Javad, Mohammad Reza Yeganehzad, and Javad Saghi "A novel comprehensive database for offline Persian
handwriting recognition." Pattern Recognition 60 (2016): 378-393.