Khurram Azeem Hashmi

E-Mail:	khurram_azeem.hashmi@dfki.de
Position:	Researcher
Phone:	+49 631 20575 3493

About:

Khurram Azeem Hashmi received his bachelor’s degree in computer science from the National University of Computer and Emerging Sciences, Pakistan, in 2016, and the M.S. degree from the Technical University of Kaiserslautern. He is currently pursuing a PhD degree with the German Research Center for Artificial Intelligence (DFKI GmbH) and the Technical University of Kaiserslautern, under the supervision of Dr Didier Stricker. His research interests include deep learning for computer vision, specifically in object detection and activity recognition. Previously, he has worked in the field of document layout understanding and post-OCR error corrections. He is also interested in the area of pattern recognition and document analysis. He has publications in reputed scientific journals like MDPI Sensors and IEEE Access. He is also a reviewer for IEEE Access and MDPI.

11 Publications by Khurram Azeem Hashmi:

Investigating Attention Mechanism for Page Object Detection in Document Images
Shivam Naik, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Applied Sciences (MDPI) 12 15 Seiten 3390-3408 MDPI Switzerland 7/2022 .
Details | Link 1 | Link 2

Toward Semi-Supervised Graphical Object Detection in Document Images
Goutham Kallempudi, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Future Internet (MDPI) 14 6 Seiten 176-198 MDPI Switzerland 6/2022 .
Details | Link 1 | Link 2

Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments
Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Sensors - Open Access Journal (Sensors) 22 Seiten 3703-3722 MDPI Switzerland 5/2022 .
Details | Link 1

CasTabDetectoRS: Cascade Network for Table Detection in Document Images with Recursive Feature Pyramid and Switchable Atrous Convolution
Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Journal of Imaging (MDPI J) 7 10 Seiten 214-237 MDPI 10/2021 .
Details | Link 1

HybridTabNet: Towards Better Table Detection in Scanned Document Images
Danish Nazir, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Antonio Fernández (Hrsg.). Applied Sciences (MDPI) 11 18 Seiten 8396-8418 MDPI Switzerland 9/2021 .
Details | Link 1

Towards Robust Object detection in Floor Plan Images: A Data Augmentation Approach
Shashank Mishra, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Applied Sciences (MDPI) 11 23 Seiten 1-22 MDPI 11/2021 .
Details | Link 1

Current Status and Performance Analysis of Table Recognition in Document Images With Deep Neural Networks
Khurram Azeem Hashmi, Marcus Liwicki, Didier Stricker, Muhammad Adnan Afzal, Muhammad Ahtsham Afzal, Muhammad Zeshan Afzal
IEEE Access (IEEE) 9 Seiten 87663-87685 IEEE 6/2021 .
Details | Link 1

A Survey of Graphical Page Object Detection with Deep Neural Networks
Khurram Azeem Hashmi, Jwalin Bhatt, Muhammad Zeshan Afzal, Didier Stricker
Applied Sciences (MDPI) 11 12 Seiten 1-21 MDPI Basel, Switzerland 6/2021 .
Details | Link 1

Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images
Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Applied Sciences (MDPI) 11 16 Seite 7610 MDPI Switzerland 8/2021 .
Details | Link 1

Guided Table Structure Recognition Through Anchor Optimization
Khurram Azeem Hashmi, Didier Stricker, Marcus Liwicki, Muhammad Noman Afzal, Muhammad Zeshan Afzal
IEEE Access (IEEE) 9 Seiten 113521-113534 IEEE 8/2021 .
Details | Link 1

Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments
Muhammad Ahmed, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Radu Danescu (Hrsg.). Sensors - Open Access Journal (Sensors) 21 15 Seiten 1-30 MDPI 2021 .
Details | Link 1 | Link 2