The phrase "areas of recognition" is correct and usable in written English.
It can be used to refer to specific fields or domains where acknowledgment or awareness is present, often in contexts like education, research, or professional development.
Example: "The conference will focus on various areas of recognition in the field of artificial intelligence."
Alternatives: "fields of acknowledgment" or "domains of awareness".
Important application areas of pattern recognition techniques are character recognition, speech analysis, image segmentation, man and machine diagnostics and industrial inspection.
These applications cover areas of speech recognition, music classification, fMRI analysis, face recognition, biomedical image analysis, watermarking, gene expression array analysis, etc.
To facilitate research in different areas of handwriting recognition in these languages, a number of standard databases have been developed and distributed.
Therefore, this class of networks possesses a good applications prospects in the areas of pattern recognition, signal and image process, automatic control.
We can categorize the research areas of speech recognition into two major classes; first, acoustic and signal processing which is very much the same for ASR in every language; second, natural language processing (NLP) which is dependent on the language.
In this paper, we have tried to emphasize the role of perception and cognition in identifying unique characteristics of handwriting of any person to screen out deceptive and true statements as a computational model in the areas of Pattern Recognition and Human Computer Interaction.
A challenging area of pattern recognition is the recognition of handwritten texts in different languages and the reduction of a volume of data to the greatest extent while preserving associations (or dependencies) between objects of the original data.
"The Pittsburgh Pattern Recognition team has developed innovative technology in the area of pattern recognition and computer vision.
Founded in 2013 by Dr. Patricia Scanlon, an ex-Bell Labs researcher and PhD with nearly 20 years experience in the area of speech recognition technologies, the young company is based on the premise that speech recognition tech built for adults, such as that most recently found in devices like the Amazon Echo or Google Home, doesn't work as well as it could do for kids.
Another acquisition in the area of image recognition technology, and another exit for a European startup to a U.S. giant.
