I'm trying to train a classifier to recognize my own signature. This is how I built my classifier

How did I collect data?

  1. Signed on a piece of paper for 50 times and created 50 images out of it.

  2. for negative test cases, I downloaded IAM Handwriting database. which contains around 600MB of handwriting data. this is to negate other possible matches.

How did I do Feature Engineering?

Step 1 : Read and convert image in gray scale. Perform median blur.

img = cv2.imread(image,0)
img = cv2.medianBlur(img,5)

Step 2 : Perform adaptive threshold followed by morphological opening to reduce noise in the image.

edges = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,11,2)    
kernel = np.ones((2,2),np.uint8)
dilation = cv2.morphologyEx(edges, cv2.MORPH_OPEN, kernel)

How did I do model training?

  1. Extracted ORB features from all the sample images for training (matrix size RX32) and used a RandomForestClassifier for training.

The problem

The accuracy of my classifier is a whopping 0.9874066374996978, but it fails to recognize almost all of my new signature samples, signed on same paper under same lighting conditions. I'm new to applied ML. What do you experts think I should check??


2 Answers 2


Using a conventional Machine learning approach to tackle this problem will lead to a lower accuracy of your model. This could easily be solved by using Deep learning, more specifically Convolutional Neural Networks (CNN).

There is no need to feature engineer on your signature images if you are using a convolution network for feature extraction. But although using convolution network, I would like you to reconsider your methods and logics because there are a number of factors that come into play while accessing a signature.

The physical act of signing a signature requires coordinating the brain, eyes, arms, fingers, muscles and nerves. So every signature needs not be same everytime.

Refer to these papers before you model your classifer.


You are effectively trying to classify images. Convolutional neural networks are much better at this task. And Keras is an easy to use library for neural networks.

Check tutorials such as: https://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/




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