{"id":250994,"date":"2023-06-12T04:34:04","date_gmt":"2023-06-12T04:34:04","guid":{"rendered":"https:\/\/imarticus.org\/?p=250994"},"modified":"2024-04-04T09:55:19","modified_gmt":"2024-04-04T09:55:19","slug":"image-recognition-and-computer-vision-extracting-information-from-images","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/image-recognition-and-computer-vision-extracting-information-from-images\/","title":{"rendered":"Image Recognition and Computer Vision: Extracting Information from Images"},"content":{"rendered":"

Image recognition is a result of the incredible fusion between artificial intelligence and computer vision that has led to the emergence of this technology. Image recognition software or applications take the help of camera technology and various AI models.<\/span><\/p>\n

Image recognition technologies can distinguish and identify objects, people, texts and so on by extracting information from the images it captures. The emergence of this technology has revolutionised the industrial platform, be it pharma companies or retail shops and made brilliant opportunities for building a <\/span>career in data analytics<\/strong><\/a>.<\/span><\/p>\n

How does computer vision help in image recognition?<\/strong><\/h2>\n

The role of computer vision<\/a><\/strong> is very pivotal in image recognition. It incorporates various processes like Optical Character Recognition (OCR) with the help of which it extracts textual data from images it captures. Two technologies are employed in this case: convolutional neural networks (CNN) and deep learning<\/a><\/strong> which generally use <\/span>Python programming<\/span>.<\/span><\/p>\n

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With the aid of machine learning, computers can distinguish between images without the necessity of detailed programming. This is done by breaking down images into minute pixels and then making predictions based on that. By undergoing repetitive iterations, the predictions are made more accurate which is almost parallel to human perception.<\/span><\/p>\n

Using CNN for image recognition<\/strong><\/h2>\n

The Convolutional Neural Network inculcates a strong algorithm which it uses for image processing. It uses three different layers for analysing the images namely, Convolutional Layer, Pooling Layer and Fully-Connected Layer.\u00a0<\/span><\/p>\n

In the Convolutional Layer, a small portion of input neurons is connected to hidden neurons. The dimensionality of the feature map is reduced by the Pooling Layer which follows the Convolutional Layer. In the end, the Fully-Connected Layer assesses the input data from the previous two layers and helps make assessments through predictions based on memory.<\/span><\/p>\n

How does image recognition work?<\/strong><\/h2>\n

There are a series of steps that are followed to convert images into textual data. These steps include:<\/span><\/p>\n