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Main commercial applications of the convolutional neural network

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The main trends in business applications using convolutional neural network are mentioned here.

The convolutional neural network is a deep learning artificial neural network. The term “convolutional” designates a mathematical function derived by integration of two distinct functions. It is about grouping different elements into a coherent whole by multiplying them. Convolution describes how the other function influences the form of a function. CNN uses optical character recognition (OCR) to classify and group particular items like letters and numbers. Optical character recognition brings these elements together into a cohesive whole.

Using CNN in Image Classification

Image recognition and classification are the main area of ​​use of convolutional neural networks. CNN deconstructs an image and identifies its distinct characteristic. For this, the system uses a supervised machine learning classification algorithm. It reduces the description to its essential references. It is performed by an unsupervised machine learning algorithm. Image tagging algorithms are the most basic type of image classification. The image tag is a word or a combination of words that describes images and makes them easier to find. Google, Facebook and Amazon use this technique. It is also one of the founding elements of visual research.

Markup includes object recognition and even sentiment analysis of the tone of the image. The visual search technique involves matching an input image with the available database. Additionally, visual search analyzes the image and finds images with similar identifying information. For example, this is how Google can find versions of the same model but in different sizes. Recommendation engines are another area for applying image classification and object recognition. For example, Amazon uses CNN image recognition for the suggestions in the “You may also like” section. The basis of the hypothesis is the behavior expressed by the user. The products themselves are matched on visual criteria like red shoes and red lipstick for the red dress. Pinterest uses CNN image recognition in a different way. The business relies on visual identifier matching, which translates into a simple visual match supplemented by labeling.

Facial recognition app using CNN

The difference between direct image recognition and facial recognition is in the operational complexity, the extra layer of work involved. First, the shape of the face and its features are recognized. Then the characteristics of the face are analyzed further to identify its essential references. For example, it could be the shape of the nose, its skin tone, texture, or the presence of scars, hairs, or other abnormalities on the surface. The sum of these identifying information is calculated in the perception of the image data of the appearance of a particular human being. This process involves studying many samples that present the subject in a different form. For example, with or without sunglasses. In social media, facial recognition helps streamline the often dubious process of tagging people in the photo. In entertainment, facial recognition lays the foundation for further transformations and manipulations. Facebook Messenger filters and Snapchat Looksery filters are the most prominent examples. Filters jump from the automatically generated basic face layout and attach new elements or effects.

Optical character recognition using CNN

Optical character recognition was designed for processing written and printed symbols. Like facial recognition, it involves a more complicated process with moving moving parts. In this process, the image is scanned for items that look like written characters, they can be specific characters or in general. Then each character is broken down into critical identifying information that identifies it as such, such as a particular form of letters “S” or “Z”. Later, the image is matched to the respective character encoding. Recognized characters are compiled into the text according to the visual layout of an input image. Tagging images and additional descriptions of image content for better indexing and navigation use CNN. Ecommerce platforms, like Amazon, are using it for greater impact. Legal organizations, such as banks and insurance companies, use handwriting optical character recognition.

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