Division of Microelectronic Systems Design (EMS)

Under way

Exploration of Deep Neural Network Topologies and Efficient Hardware Architecture for Historical Document Baseline Detection

Type of work:

Master Thesis / Diplomarbeit


In this work, a student has to search literature for the state-of-the-art deep learning based methods for baseline detection of historical handwritten documents. As the next step, the method with the best trade-off between complexity and accuracy has to be implemented in PyTorch using Python. A student has to explore trade-offs between complexity and accuracy for the implemented model using complexity reduction techniques. A hardware architecture has to be designed and implemented using Xilinx Vivado tools.


  • Interest in Deep Learning and FPGAs
  • Experience with Python and C/C++
  • Experience with PyTorch and Xilinx Vivado HLS


Baseline detection of historical documents is an important step in historical documents recognition chain that is used to digitize historical documents and preserve the knowledge from disappearance.


V. Rybalkin


Ahmed EL-Yamany



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