Helping clients formulate and implement artificial intelligence driven recognition systems
Though many exist, the most common types of recognition systems are focused on these 3 areas.
The most commonly known AI is the Biometric Identification Recognition System. This form of AI is capable of identifying physical and biological characteristics. An example of this would be fingerprint recognition, and voice recognition. Using a fingerprint to unlock a phone or to use a phone’s voice-activated assistant are examples of this form of recognition.
Handwritten text recognition is used to interpret hard to read handwriting from paper documents or process handwriting used with a stylus in certain software programs. This form of AI is often used in healthcare for creating digital prescription records and in banking to process handwritten checks. The goal is to reduce human error and create efficiency. Using handwriting recognition in AI is a work in progress as every single individual has their own unique handwriting.
An image retrieval system is used to find images, generally from a large database. These images are located through added metadata, keywords, image description, or image title. There are two types of image retrieval. Relevant image retrieval will pull images that may be relevant to the original image search. Exact image retrieval pulls up the exact images needed based on a specific search query.
Predicted market size for the recognition systems industry.
2018 Gartner “Forecast Analysis: Information Security”
Creating an AI recognition system must go through a few different planning stages.
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.
“Cloud computing is increasingly becoming a vehicle for next-generation digital business as well as for agile, scalable and elastic solutions.”
David Mitchell Smith,
Vice President and Gartner Fellow
Global Optical Character Recognition ( OCR ) Systems Market is predicted to go from $10,470 million to $26,964 million by 2030.
Verfied Market Research (2022)
Senior consultants with previous experience with these types of projects can set the stage for a well-framed engagement.
A focused session on your specific software applications, platforms, or projects. Typically this includes technical resources from both sides.