  Human vision can distinguish between a vast spectrum of colours, estimated to
be between 2 to 7 million discernible shades. However, this impressive range
does not inherently imply that all these colours have been precisely named and
described within our lexicon. We often associate colours with familiar objects
and concepts in our daily lives. This research endeavors to bridge the gap
between our visual perception of countless shades and our ability to articulate
and name them accurately. A novel model has been developed to achieve this
goal, leveraging Bidirectional Long Short-Term Memory (BiLSTM) networks with
Active learning. This model operates on a proprietary dataset meticulously
curated for this study. The primary objective of this research is to create a
versatile tool for categorizing and naming previously unnamed colours or
identifying intermediate shades that elude traditional colour terminology. The
findings underscore the potential of this innovative approach in
revolutionizing our understanding of colour perception and language. Through
rigorous experimentation and analysis, this study illuminates a promising
avenue for Natural Language Processing (NLP) applications in diverse
industries. By facilitating the exploration of the vast colour spectrum the
potential applications of NLP are extended beyond conventional boundaries.
