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diff --git a/README.md b/README.md
index ec358cb..98aa7c6 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
 
 EXTRACT is an optical character recognition engine for various operating systems which extracts texts from an image and converts them to plain text.
 
-This model is a very primitive form of the original google tesseract which extracts texts (ONLY CAPITAL LETTERS) from an image and converts them to plain text.
+This model is a very primitive form of the original google tesseract which extracts texts from an image and converts them to plain text.
 
 ## Modules/Library REQUIREMENTS:
 
@@ -17,12 +17,19 @@ This model is a very primitive form of the original google tesseract which extra
   7) cv2
   8) shutil
   
+## Features
+ 
+a)  Extracts text from input image
+
+b) Works on lowercase,uppercase, number ans special characters.
+
+c) Saves the output in output.txt to allow search.
+
 ## How To Run the script:
 
 NOTE1:- The trained model is not provided. So for the very first time run the script as it is. Once the model is trained:
-                                          COMMENT OUT 'Train_Model' on line '65' and then run the script for further use.
+                                          COMMENT OUT 'Train_Model()' then run the script for further use.
                                           
-NOTE2:- Only some fonts were taken into account so remember to use default font (calibri) in image texts with a FONT SIZE of '72' as there are assumptions to extract letters.
 
 ![](sentences/sentence_format.png)
 
@@ -38,3 +45,16 @@ The input image can be of any number of words example:
 
 output is:
 ![](sentences/terminal_output.png)
+
+
+## Contributors
+
+- Akarsh Malik
+- Angad Ripudaman Singh Bajwa
+
+## Future Work
+
+1)  To add characters of your own, make sure to add them in the train and test dataset
+2) Change the output of the softmax layer in Train_Model function to the total number of trained characters.
+2) Re-train the model
+3) Test your image
diff --git a/sentences/dwoc.png b/sentences/dwoc.png
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diff --git a/sentences/ex5.png b/sentences/ex5.png
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index 0000000..7fad676
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diff --git a/sentences/ex6.png b/sentences/ex6.png
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index 0000000..5793f80
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diff --git a/sentences/ex7.png b/sentences/ex7.png
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diff --git a/sentences/ex8.png b/sentences/ex8.png
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diff --git a/tesseract.py b/tesseract.py
index b661009..64898d9 100644
--- a/tesseract.py
+++ b/tesseract.py
@@ -1,99 +1,146 @@
+'''
+Importing required libraries
+'''
 import os
-import sys
-import shutil
-import time
 import numpy as np
-from PIL import Image,ImageTk
+from PIL import Image, ImageTk
 import PIL
+import sys
 from keras.preprocessing.image import ImageDataGenerator
 from keras.applications import VGG16
-from keras.layers import Dense,Flatten
+from keras.layers import Dense, Flatten, Dropout, BatchNormalization
 from keras.models import Sequential, load_model
-import cv2
+from keras.optimizers import SGD, Adam
+from keras.regularizers import l2
 from cropyble import Cropyble
+import cv2
+import shutil
+import time
+import pytesseract
+
+'''
+Enter location of pytesseract (if error msg displayed)
+'''
+#pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
 
+
+
+'''
+Input the image
+'''
 location = input("Enter the location of the image : ")
-image = cv2.imread(location,0)
+image = cv2.imread(location, 0)
 thresh = cv2.threshold(image, 150, 255, cv2.THRESH_BINARY_INV)[1]
 
-result = cv2.GaussianBlur(thresh, (5,5), 0)
+result = cv2.GaussianBlur(thresh, (5, 5), 0)
 result = 255 - result
 
-cv2.imwrite('final_img.png',result)
-loc = 'final_img.png'
+cv2.imwrite('final_img.png', result)
 
+loc = 'final_img.png'
+if os.path.isfile(loc):
+    print("File exists")
 
 if os.path.isfile('DATA/train/.DS_Store'):
     os.remove('DATA/train/.DS_Store')
 if os.path.isfile('DATA/test/.DS_Store'):
     os.remove('DATA/test/.DS_Store')
-    
-for i in range(1,80):
+
+for i in range(1, 80):
     if os.path.isfile(f'DATA/train/{i}/.DS_Store'):
         os.remove(f'DATA/train/{i}/.DS_Store')
     if os.path.isfile(f'DATA/test/{i}/.DS_Store'):
         os.remove(f'DATA/test/{i}/.DS_Store')
 
-datagen = ImageDataGenerator(rescale = 1./255,
-                            zoom_range = 0.2)
-
+datagen = ImageDataGenerator(rescale=1. / 255,
+                             zoom_range=0.2)
 
-trained_image = datagen.flow_from_directory('DATA/train',
-                                            target_size = (32,32),
-                                            class_mode = 'categorical')
+trained_image = datagen.flow_from_directory('DATA/train/',
+                                            target_size=(32, 32),
+                                            class_mode='categorical')
 
-test_datagen = ImageDataGenerator(rescale = 1./255)
+test_datagen = ImageDataGenerator(rescale=1. / 255)
 
-test_image = test_datagen.flow_from_directory('DATA/test',
-                                            target_size = (32,32),
-                                            class_mode = 'categorical')
+test_image = test_datagen.flow_from_directory('DATA/test/',
+                                              target_size=(32, 32),
+                                              class_mode='categorical')
 
 
 def Train_Model():
+    '''
+
+    Function to train model using VGG16 weights
+    To train the model un-comment the Train_Model() after the function
+
+    '''
+    reg = l2(0.010)
     global trained_image, test_image
     model = Sequential()
-    conv_base = VGG16(weights='imagenet', include_top = False, input_shape = (32,32,3))
+    conv_base = VGG16(weights='imagenet', include_top=False, input_shape=(32, 32, 3))
     model.add(conv_base)
     model.add(Flatten())
-    model.add(Dense(256,activation='relu'))
-    model.add(Dense(128,activation='relu'))
-    model.add(Dense(26,activation = 'softmax'))
-    model.compile(optimizer= 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
-    model.fit(trained_image, epochs = 30, validation_data = test_image, validation_steps = 1)
+    # model.add(Dense(1024, activation=('relu'), input_dim=512, kernel_regularizer=reg))
+    model.add(Dense(512, activation=('relu'), kernel_regularizer=reg))
+    model.add(Dropout(.3))
+    model.add(Dense(256, activation=('relu'), kernel_regularizer=reg))
+    model.add(Dropout(.3))
+    model.add(Dense(128, activation=('relu'), kernel_regularizer=reg))
+    model.add(Dropout(.2))
+    model.add(Dense(79, activation=('softmax')))
+    model.summary()
+    learn_rate = .001
+    sgd = SGD(lr=learn_rate, momentum=.9, nesterov=False)
+    # adam = Adam(lr=learn_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
+    model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
+    model.fit(trained_image, epochs=30, validation_data=test_image, validation_steps=1)
     model.save('model.h5')
-    
-Train_Model()
+
+
+#Un-comment next line to train model
+#Train_Model()
 
 
 def Predict_Model(img):
+    '''
+    Function to predict the class of the character
+    '''
     global trained_image
     new_model = load_model('model.h5')
-    img = cv2.resize(img,(32,32),3)
-    img = np.expand_dims(img,axis=0)
+    img = cv2.resize(img, (32, 32), 3)
+    img = np.expand_dims(img, axis=0)
     img = img / 255
-    letters = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z','A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z','1','2','3','4','5','6','7','8','9','0',',',';',':','?','!','.','@','#','$','%','&','(',')','{','}','[',']']
-    
+
+    dict_og= {'!': 0, '#': 1, '$': 2, '%': 3, '&': 4, '(': 5, ')': 6, ',': 7, '0': 8, '1': 9, '2': 10, '3': 11, '4': 12, '5': 13,
+     '6': 14, '7': 15, '8': 16, '9': 17, ';': 18, '=': 19, '@': 20, 'A': 21, 'B': 22, 'C': 23, 'D': 24, 'E': 25,
+     'F': 26, 'G': 27, 'H': 28, 'J': 29, 'K': 30, 'L': 31, 'M': 32, 'N': 33, 'O': 34, 'P': 35, 'Q': 36, 'R': 37,
+     'S': 38, 'T': 39, 'U': 40, 'V': 41, 'W': 42, 'Y': 43, 'Z': 44, '[': 45, ']': 46, 'X': 47, '{': 48, '}': 49,
+     'u': 50, 'j': 51, 'k': 52, 'r': 53, 's': 54, 't': 55, 'f': 56, '?': 57, 'q': 58, 'l': 59, '.': 60, 'I': 61,
+     'g': 62, '̣h': 63, 'x': 64, 'w': 65, 'a': 66, 'b': 67, 'm': 68, 'n': 69, 'p': 70, 'v': 71, 'y': 72, 'z': 73,
+     'i': 74, 'o': 75, 'e': 76, 'd': 77, 'c': 78}
+
     prediction = new_model.predict_classes(img)
     prediction = prediction[0]
-    
-    my_dict = dict(trained_image.class_indices)
-    
-    for key,value in my_dict.items():
+
+    #my_dict = dict(trained_image.class_indices)
+
+    for key, value in dict_og.items():
         if prediction == value:
             return key
 
 
 def Word_Extract(location):
-    
+    """
+    Function to extract words from the input image using Cropyble and OpenCV
+    """
     if os.path.isdir('WORDS'):
         shutil.rmtree('WORDS')
         os.mkdir('WORDS')
     else:
         os.mkdir('WORDS')
-        
+
     my_img = Cropyble(location)
     img = PIL.Image.open(location)
-    
+
     words = my_img.get_words()
     select = []
     for i in range(len(words)):
@@ -101,90 +148,105 @@ def Word_Extract(location):
             if (len(words[i]) > 1):
                 select.append(words[i])
             elif (len(words[i]) == 1):
-                if ( i != 0):
-                    if (len(words[i-1]) > 1 and len(words[i+1]) > 1):
+                if (i != 0):
+                    if (len(words[i - 1]) > 1 and len(words[i + 1]) > 1):
                         select.append(words[i])
-                elif ( i == 0):
+                elif (i == 0):
                     select.append(words[i])
 
-                    
     j = 0
 
     for i in select:
         rect = my_img.get_box(i)
-        crop_img = img.crop((rect[0]-20,rect[1]-20,rect[2]+20,rect[3]+20))
+        crop_img = img.crop((rect[0] - 20, rect[1] - 20, rect[2] + 20, rect[3] + 20))
         crop_img.save(f'WORDS/{j}.png')
         j += 1
-    
+
     return my_img, select
-      
-    
-cropyble_img,select = Word_Extract(loc)
 
 
+cropyble_img, select = Word_Extract(loc)
+
 if os.path.isdir('LETTERS'):
     shutil.rmtree('LETTERS')
     os.mkdir('LETTERS')
 else:
     os.mkdir('LETTERS')
-    
-    
+
+
+'''    
+Extracting the letters from the WORDS
+'''
 for i in range(len(os.listdir('WORDS'))):
     os.mkdir(f'LETTERS/{i}')
-    image = cv2.imread(f"WORDS/{i}.png") 
-    gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) 
-    edged = cv2.Canny(image, 10, 250) 
-    (cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 
-    idx = 0 
-    for c in cnts: 
-        x,y,w,h = cv2.boundingRect(c) 
-        if w>50 and h>50: 
-            idx+=1 
-            new_img=image[y:y+h,x:x+w] 
-            cv2.imwrite(f"LETTERS/{i}/" + str(idx) + '.png', new_img) 
-            
+    image = cv2.imread(f"WORDS/{i}.png")
 
+    edged = cv2.Canny(image, 10, 250)
+    (cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    idx = 0
+    for c in cnts:
+        x, y, w, h = cv2.boundingRect(c)
+        if 10 < w and 10 < h:
+            idx += 1
+            new_img = image[y:y + h, x:x + w]
+            cv2.imwrite(f"LETTERS/{i}/" + str(idx) + '.png', new_img)
+
+
+
+'''
+ Passing each letter to the Predict_Model() and getting output class prediction
+'''
 
 text = ''
 for i in range(len(os.listdir('LETTERS'))):
     string = ''
     char_dict = {}
     for j in range(len(os.listdir(f'LETTERS/{i}'))):
-        img = cv2.imread(f'LETTERS/{i}/{j+1}.png')
-        img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+        img = cv2.imread(f'LETTERS/{i}/{j + 1}.png')
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         char = Predict_Model(img)
-        
-        
+
         for l in range(len(select[i])):
+
             if char == select[i][l]:
                 char_dict[select[i].index(char)] = char
-                
-                
+
+
+
             else:
                 for k in range(len(select[i])):
                     if k != l:
                         if char == select[i][k]:
-                            char_dict[k] = char 
-        
+                            char_dict[k] = char
+
+
+
         keys = list(char_dict.keys())
         if char not in select[i]:
             for val in range(len(select[i])):
                 if val not in keys:
                     char_dict[val] = char
 
+
     keys = list(char_dict.keys())
     keys.sort()
-    #print(keys)
-    
+
     for m in keys:
         string += char_dict[m]
-        
-        
+
     string += ' '
     text += string
-              
 
-for i in range(10):
+
+
+'''
+Output
+'''
+for i in range(5):
     print("")
-print("The predicted sentence is: ",text)
-    
+print("The predicted sentence is: ", text)
+
+with open('output.txt', 'w') as file:
+    file.write("The predicted sentence is:  ")
+    file.write(text)
+    file.close()
\ No newline at end of file