Abhishek Bhandari Ph.D. Researcher
Bridging Indic languages and computer vision through robust NLP
PhD Researcher in Computer Science & Engineering

I am currently pursuing a PhD in Computer Science & Engineering at Indian Institute of Technology Jodhpur. My research focuses on Natural Language Processing and Computer Vision.

I design methods for contextual text error correction in Indic languages, multi-modal knowledge extraction, and low-resource learning.

Contextual Text Correction Indic Language Processing

Education
  • Indian Institute of Technology Jodhpur
    Indian Institute of Technology Jodhpur
    Computer Science & Engineering
    Ph.D. Student
    2020 - 2026 (expected)
  • University of Hyderabad
    University of Hyderabad
    M.Tech. in Information Technology
    2017 - 2019
  • Government Engineering College Ajmer
    Government Engineering College Ajmer
    B.Tech. in Information Technology
    2012 - 2016
Selected Publications (view all )
Mind the (Language) Gap: Towards Probing Numerical and Cross-Lingual Limits of LVLMs
Mind the (Language) Gap: Towards Probing Numerical and Cross-Lingual Limits of LVLMs

S Gautam, AS Penamakuri, A Bhandari, G Harit

Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)

This paper introduces MMCRICBENCH-3K, a benchmark for Visual Question Answering on cricket scorecards designed to evaluate large vision-language models on complex numerical and cross-lingual reasoning over semi-structured tabular images. Empirical results show that state-of-the-art models struggle with structure-aware numerical reasoning and cross-lingual generalization.

Mind the (Language) Gap: Towards Probing Numerical and Cross-Lingual Limits of LVLMs

S Gautam, AS Penamakuri, A Bhandari, G Harit

Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)

This paper introduces MMCRICBENCH-3K, a benchmark for Visual Question Answering on cricket scorecards designed to evaluate large vision-language models on complex numerical and cross-lingual reasoning over semi-structured tabular images. Empirical results show that state-of-the-art models struggle with structure-aware numerical reasoning and cross-lingual generalization.

TabComp: A Dataset for Visual Table Reading Comprehension
TabComp: A Dataset for Visual Table Reading Comprehension

S Gautam, A Bhandari, G Harit

Findings of the Association for Computational Linguistics: NAACL 2025

This paper introduces TabComp, a dataset for visual table reading comprehension. The dataset is designed to advance research in understanding and extracting information from tables in documents.

TabComp: A Dataset for Visual Table Reading Comprehension

S Gautam, A Bhandari, G Harit

Findings of the Association for Computational Linguistics: NAACL 2025

This paper introduces TabComp, a dataset for visual table reading comprehension. The dataset is designed to advance research in understanding and extracting information from tables in documents.

Reversible data hiding using multi-layer perceptron based pixel prediction
Reversible data hiding using multi-layer perceptron based pixel prediction

A Bhandari, S Sharma, R Uyyala, R Pal, M Verma

Proceedings of the 11th International Conference on Advances in Information Technology 2020

This paper presents a novel approach for reversible data hiding using multi-layer perceptron for pixel prediction. Reversible data hiding is a technique that allows the original cover media to be perfectly restored after the hidden data has been extracted.

Reversible data hiding using multi-layer perceptron based pixel prediction

A Bhandari, S Sharma, R Uyyala, R Pal, M Verma

Proceedings of the 11th International Conference on Advances in Information Technology 2020

This paper presents a novel approach for reversible data hiding using multi-layer perceptron for pixel prediction. Reversible data hiding is a technique that allows the original cover media to be perfectly restored after the hidden data has been extracted.

All publications