Convolutional Neural Network, is an artificial deep learning neural network. The term "convolutional" means mathematical function derived by integration from two distinct functions. It includes rolling different elements together into a coherent whole by multiplying them. Convolution describes how the other function influences the shape of one function. CNN uses Optical Character Recognition (OCR) to classify and cluster peculiar elements like letters and numbers. Optical Character Recognition puts these elements together into a coherent whole.
Image recognition and classification is the primary field of convolutional neural networks use. CNN deconstructs an image and identifies its distinct feature. For that, the system uses a supervised machine learning classification algorithm. It reduces the description of its essential credentials. It is done 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 word combination that describes the images and makes them easier to find. Google, Facebook, and Amazon are using this technique. It is also one of the foundation elements of visual search.
Tagging includes recognition of objects and even sentiment analysis of the picture tone. The technique of visual search involves matching an input image with the available database. Besides, the visual search analyzes the image and looks for images with similar credentials. For example, this is how Google can find versions of the same model but in different sizes. Recommender engines is another field to apply image classification and object recognition. For example, Amazon uses CNN image recognition for suggestions in the "you might also like" section. The basis of the assumption is the user's expressed behavior. The products themselves are matched on visual criteria like red shoes and red lipstick for the red dress. Pinterest uses image recognition CNN in a different way. The company relies on visual credentials matching, and this results in a simple visual matching supplemented with tagging.
The difference between straight image recognition and face recognition lays in operational complexity, the extra layer of work involved. At first, the shape of the face and its features are recognized. Then the features of the face are further analyzed to identify its essential credentials. For example, it can be the shape of the nose, its skin tone, texture, or presence of scar, hair, or other anomalies on the surface. The sum of these credentials is calculated into the image data perception 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 networking, face recognition serves as a streamlining of the often-dubious process of tagging people in the photo. In entertainment, face recognition lays the groundwork for further transformations and manipulations. Facebook Messenger's filters and Snapchat Looksery filters are the most prominent examples. The filters jump from the autogenerated basic layout of the face and attach new elements or effects.
Optical Character Recognition was designed for written and print symbol processing. Like face recognition, it involves a more complicated process with move moving parts. In this process the image is scanned for elements that resemble written characters, it can be specific characters or in general. Then each character is broken down into critical credentials that identify it as such, like a particular shape of letters "S" or "Z." Later the image is matched with the respective character encoding. The recognized characters are compiled into the text according to the visual layout of an input image. Image tagging and further descriptions of the image content for better indexing and navigation are using CNN. The Ecommerce platforms, such as Amazon, are using it for a more significant impact. Legal organizations, as banking and insurance, use Optical Character Recognition of handwriting.