Recognition in an obscure world

When developing a recognition system, it is important to decide the objective of the project. Once there is a design scope, the next step will be how to determine the type of tasks the system will perform and what type of information will be gathered. Recognition systems are often designed for companies to process images of products or people.

Gathering the data that will be used for training is important. Using clean and correctly annotated data such as photos, audio clips, and metatags allows for efficiency and accuracy. The selected data is what will be used to train the system and create neural pathways that will create a pattern of recognition.

Once data has been collected and properly identified, development and training is a significant steps toward creating a recognition system. For example, a retail website must be able to correctly distinguish between bags and shoes.  This retailer will want to develop a system that is trained on correctly tagging and identifying the defining characteristics of its merchandise. With the right training, the machine will intelligently be able to recognize image content based on familiar images and tags.

Once the system has been built to recognize objects or items outside of its training set, it must go through extensive testing. These recognition systems must be tested for general bugs and errors, but also performance and fringe cases. The system must have accurate and timely responses.

Once the recognition system has been built and tested, the final phase is the launch phase, also known as deployment. At launch, the recognition system will be supplemented with ease-of-use dashboards and performance indicators.