Khurram Azeem Hashmi
|Phone:||+49 631 20575 3493|
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.
CasTabDetectoRS: Cascade Network for Table Detection in Document Images with Recursive Feature Pyramid and Switchable Atrous Convolution
Journal of Imaging (MDPI J) 7 10 Seiten 214-237 MDPI 10/2021 .
HybridTabNet: Towards Better Table Detection in Scanned Document Images
Antonio Fernández (Hrsg.). Applied Sciences (MDPI) 11 18 Seiten 8396-8418 MDPI Switzerland 9/2021 .
Towards Robust Object detection in Floor Plan Images: A Data Augmentation Approach
Applied Sciences (MDPI) 11 23 Seiten 1-22 MDPI 11/2021 .
Current Status and Performance Analysis of Table Recognition in Document Images With Deep Neural Networks
IEEE Access (IEEE) 9 Seiten 87663-87685 IEEE 6/2021 .
A Survey of Graphical Page Object Detection with Deep Neural Networks
Applied Sciences (MDPI) 11 12 Seiten 1-21 MDPI Basel, Switzerland 6/2021 .
Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images
Applied Sciences (MDPI) 11 16 Seite 7610 MDPI Switzerland 8/2021 .
Guided Table Structure Recognition Through Anchor Optimization
IEEE Access (IEEE) 9 Seiten 113521-113534 IEEE 8/2021 .
Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments
Radu Danescu (Hrsg.). Sensors - Open Access Journal (Sensors) 21 15 Seiten 1-30 MDPI 2021 .