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@article{150952,
        author = {Vunnava Gunwanth Chowdary and Anil Kumar Gupta and Saket Kumar Jha},
        title = {An Intelligent Sign board detection and recognition system on Computer vision and NLP: A Survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {11},
        pages = {51-56},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=150952},
        abstract = {Parking signs acknowledgment and confinement intend to extricate and digitize exact on-road Parking limitations. The present visual information assortment, comment, and examination rehearses are still exorbitant, powerless to blunder, and awkward as performed physically. While online road level symbolism information bases contain refreshed all-encompassing pictures, all things considered, their potential for comprehension on-road Parking limitations at scale has not been completely investigated. The key favourable position of these information bases is that when the Parking signs are identified, precise geographic directions of the recognized signs are regularly naturally decided and imagined inside an equal stage. This paper assesses the machine of a computer vision strategy for Parking signs acknowledgment from road level symbolism pointed toward encouraging the Parking tricks of urban communities. Tricks such as vehicle to be parked at specific period of time at specific dates and theses specific conditions various. The likely recognitions of pictures from various perspectives are then consolidated to find the conditions of the signs on a guide. NLP (Natural Language Processing) is used as text extraction from the recognized parking signs and displays a result based on text recognition weather we can park or not on specific time and date. So, this exhibit the capability of utilizing road level pictures and flexibly a practical answer for digitizing at scale all parking signs to help drivers comprehend parking rules and keep away from fines.},
        keywords = {computer vision, NLP (Natural Language Processing), text extraction, text recognition},
        month = {},
        }