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Baseline detection of historical documents is an important step in historical documents recognition chain that is used to digitize historical documents and preserve the knowledge from disappearance.