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OCR_MLP.py

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+
+# coding: utf-8
+
+# In[21]:
+
+
+import sys
+import os
+
+import cv2
+import numpy as np
+
+
+# In[39]:
+
+
+input_file='Desktop/summer project/letter.data'
+img_height=16
+img_width=8
+img_resize_factor=22
+
+
+# In[ ]:
+
+
+labels=[]
+with open(input_file,'r') as f:
+    for line in f.readlines():
+        data=np.array([255*float(x) for x in line.split('\t')[6:-1]])
+        image_label=line.split('\t')[1]
+        if image_label not in labels:
+            labels.append(image_label)
+        image=np.reshape(data,(img_height,img_width))
+        image_scaled=cv2.resize(image,None,fx=img_resize_factor,fy=img_resize_factor)
+        cv2.imshow('IMG',image_scaled)
+        print('Label : ',image_label)
+        print(len(data))
+        wkey=cv2.waitKey()
+        if wkey==27:
+           break
+
+
+# In[ ]:
+
+
+num_data = 50
+orig_labels = 'omandig'
+num_orig_labels = len(orig_labels)
+
+num_train = int(0.9*num_data)
+num_test = num_data - num_train
+
+start = 6
+end = -1
+
+
+# In[ ]:
+
+
+data = []
+labels = []
+
+with open(input_file, 'r') as f:
+    for line in f.readlines():
+        list_vals = line.split('\t')
+        if list_vals[1] not in orig_labels:
+            continue
+        label = np.zeros((num_orig_labels,1))
+        label[orig_labels.index(list_vals[1])]=1
+        labels.append(label)
+
+        cur_char = np.array([float(x) for x in list_vals[start:end]])
+        data.append(cur_char)
+
+        if len(data) >= num_data:
+            break
+
+
+
+# In[ ]:
+
+
+data_r=(np.array(data).reshape(50,128))
+labels_r=np.array(labels).reshape(50,num_orig_labels)
+labels_r[0].shape
+
+data_train=data_r[:num_train]
+data_test=data_r[num_train:]
+labels_train=labels_r[:num_train]
+labels_test=labels_r[num_train:]
+
+
+# In[130]:
+
+
+from sklearn.neural_network import MLPClassifier as MLP
+nn=MLP(hidden_layer_sizes=(128,16,num_orig_labels),max_iter=20000,tol=0.01)
+nn=nn.fit(data_train,labels_train)
+nn.score(data_test,labels_test)
+