Advanced Image Recognition: what it is and how to exploit it

Advanced Image Recognition

In the field of artificial intelligence, we often hear of advanced image recognition (AIR), a particular technology that, through exploiting convolutional neural networks, simulates the human brain with regard to sight. In practice, artificial neural networks, increasingly similar to natural ones, have favored the evolution of image recognition.

AIR technology is one of the applications of artificial intelligence to improve business organization as provided by Humable, where this is able to offer a range of tangible benefits to customers, including:

  • troubleshooting, as all documents and pictures are strictly inspected, processed and organized without any human intervention;
  • flexible technology that easily adapts to every type of document and every need;
  • extracting more information by accelerating digital automation projects.

The companies that have relied on us have seen errors decrease by 36% and an improvement in productivity of 23%. Just consider that one minute of a robot’s work is equivalent to 15 minutes of manual work, so such solutions also cut downtime, hence accelerating the productivity of your company.

The evolution of advanced image recognition

Advanced Image Recognition

AIR technology has developed over the decades, giving life to the so-called artificial neural network (ANN). It is a parallel computation model composed of different processing units and interconnected with one another by way of connections of various strengths.

In practice, there exist input units that perform a series of actions. Firstly, they collect the data on the problem to be processed; thereafter, processing takes place, where this propagates parallel to the output units, which give rise to the final result. An ANN is not programmed to solve a single problem, but must be trained following concrete examples of the reality to be modeled.

What is image recognition and how does it work?

When we speak of advanced image recognition systems, we refer to this simply as image recognition. But, what is this actually all about?

Today, though exploiting advanced neural networks, artificial intelligence is able to recognize images by perfectly imitating the behavior of human sight. Experiments conducted on the brain have made it possible to highlight that the primary visual cortex is composed of a set of simple neuronal structures that enable sight to be activated.

Starting with this concept, using sophisticated algorithms for reading and interpreting images, machines are able to exploit neural networks, making it possible to recognize shapes, colors and even follow moving objects.

Image recognition is primarily based on CNNs (convolutional neural networks), which help to classify images, made up of pixels, and then extract a series of characteristics and information.

After each image has been converted into thousands of features, the model is trained, broadly retracing the same operation as machine learning. As more images are used, the model improves, where it is essentially trained to determine whether a given image represents a compliant product or not.

After having been suitably trained, each model can subsequently be used to recognize unknown images, reaching a remarkable level of versatility depending on the requirements.

The various steps of CNN

To better clarify the concept of CNN, below is a summary of the mechanism used to recognize an object or image:

  • the first layer deals with detecting lines, edges and changes in brightness;
  • the information is passed on to the next level which, by combining the previous functions, creates detectors aimed at identifying simple shapes;
  • the process continues in levels, becoming increasingly abstract and therefore able to detect specific objects;
  • the last layers of the network integrate all these complex characteristics and then draw up classification forecasts;
  • the expected value is initially compared to the output and incorrect values ​​are found; the network thus proposes the learning process to obtain a more precise result;
  • the network corrects itself automatically until a satisfactory result is obtained, which minimizes or eliminates errors.

How to get the most out of your data with AIR: Humable explains

By taking advantage of the AIR solution that we at Humable make available to you, you can “read” the data present in an image. The system inspects the image, then operates in total autonomy, as a result of advanced optical character recognition, artificial intelligence and a series of cutting-edge techniques, where the outcome is identifying and extracting valuable information from unstructured content.

A special bot inspects the document, reads it and extracts the most important content, and then organizes and transfers this according to your specific needs. The process is completed with an accurate data check that provides a broader view. Do you need to transform images and texts digitally? Humable is the right solution for you.

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