Priyanshu Jain

Priyanshu Jain

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Product Engineer @Sprinklr | GSoC'21 @KODI | IIITA’23
Gurugram, Haryana, India

Contact Priyanshu regarding: 
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Starting at USD75k/year

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Résumé


Jobs verified_user 0% verified
  • Sprinklr
    Product Engineer
    Sprinklr
    Jul 2023 - Current (3 years 4 months)
  • Sprinklr
    Product Engineering Intern
    Sprinklr
    Jan 2023 - Jun 2023 (6 months)
  • DaveAI
    Software Engineer Intern
    DaveAI
    Oct 2021 - Jan 2022 (4 months)
  • Google Summer of Code
    Student Developer @KODI
    Google Summer of Code
    May 2021 - Aug 2021 (4 months)
    - Migrated CoffeeScript components to Elm as part of system stability improvements for the Kodi project. - Integrated frontend web interfaces with backend functionalities, resolving notable existing bugs and enhancing application performance.
  • Eulogik
    Flutter Developer Intern
    Eulogik
    Nov 2020 - Jan 2021 (3 months)
Education verified_user 0% verified
  • Indian Institute of Information Technology
    Bachelor's degree, Information Technology
    Indian Institute of Information Technology
    Jan 2019 - Dec 2023 (5 years)
    - Teaching Assistant for competitive coding classes in IIITA - Achieved ranks in Code Forces, Code Chef and Leetcode competitive contests. - Mentoring juniors
Publications verified_user 0% verified
  • D
    HH-CompWordNet: Holistic Handwritten Word Recognition in the Compressed Domain
    Data Compression Conference DCC
    Mar 2021
    Holistic word recognition in handwritten documents is an important research topic in the field of Document Image Analysis. For some applications, given strong language models, it can be more robust and computationally less expensive than character segmentation and recognition. This paper presents HH-CompWordNet, a novel approach to applying a Convolutional Neural Network (CNN) to directly to the DCT coefficients of the compressed domain word images. The efficacy of the HH-CompWordNet is demonstrated with the JPEG compressed version of the CMATERdb2.1.2.1 dataset - standard Handwritten Bangla word images. Experiments show the system obtains state-of-the-art accuracy of 86.80%.