Neural Networks in OCR

A neural network in technology is a series of artificial neurons that can learn to recognize underlying relationships in data and information.  It is used in AI and computer vision to identify and classify patterns in information using algorithms. 

Neural networks are designed to mimic the function of the human brain in recognizing relationships with a vast amount of information. They resemble the connection of neurons and synapses found in the human brain. And like humans, neural networks learn through their past experiences. 

Some OCR technologies use neural networks as a classification and even recognition tool. In converting an image of text, the OCR system first breaks the image into sub-images – each having a single character. Then, it translates the sub-images from an image format into a binary format, represented by 0 or 1 in each pixel. 

With the available binary format, the neural networks associate the character image data with a numeric value that matches its corresponding character. Lastly, the output from the neural network is translated into a machine-readable text ready for extraction. 

Continuous training of the neural network to recognize patterns from a training set improves the OCR’s accuracy in converting images of written text into machine-encoded text ready for storage or access.  

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