Computers can recognize images through a process called image recognition or computer vision. At the heart of this technology lies deep learning algorithms, specifically convolutional neural networks (CNNs), which mimic the human brain’s processing of visual information.
These algorithms are trained using vast amounts of labeled data, such as thousands or even millions of images, and learn to identify patterns and features within them. During training, the CNN assigns weights to different parts of an image, focusing on edges, colors, textures, and shapes that help it distinguish between different objects or entities.
Computers also recognize handwritten text and numbers using neural networks by leveraging the power of machine learning algorithms. These algorithms are trained on a vast amount of labeled data, which consists of samples of handwritten characters and their corresponding labels.
Initially, the neural network is presented with an image of a handwritten character, such as a letter or a number. The network then processes this image through multiple layers of interconnected computational nodes called neurons. Each neuron applies a mathematical operation to the input it receives and passes the result to the next layer until an output layer is reached.
During training, the network adjusts its internal parameters based on how well it predicts the correct label for each image in its training dataset. This iterative process helps fine-tune the network’s ability to recognize patterns in handwritten text and numbers, improving its accuracy over time.
Once trained, the neural network can efficiently analyze new images containing handwritten characters and provide accurate recognition based on patterns learned during training. At the link below you can see an example of code in Python training a neural network to predict the values of numbers that have been handwritten by thousands of different people.
