Supervisors: Florian Kleber, Marco Peer Status: open The analysis of artistic styles is a well-established challenge in the Cultural Heritage field. Traditional artworks, such as statues and paintings, have been thoroughly studied, leading to standardized methods for identifying an artist’s style. However, historical violins present a unique case. While their main stylistic features are known, … Continue reading Stylistic Classification and Retrieval of Historical Violins and Violin Makers →

The CVL offers bachelor/master theses or student projects in the domain of deep-learning-based document analysis for papyri. Supervisor: Marco Peer Status: taken First Picture: Vesuvius Challenge Second Picture: Peer and Sablatnig, HIP’23 Third picture modified from FAU Erlangen Motivation Greek papyri, ancient documents made from a type of paper, offer valuable insights into the past. … Continue reading Deep Learning for Papyri →

Status: taken Supervisors: Marco Peer The digitization and preservation of historical documents rely on accurate transcription of handwritten text. However, historical documents often present unique challenges due to variations in writing styles and deteriorated conditions. This thesis should explore the concepts of writer identification and writer-specific style extraction within Handwritten Text Recognition (HTR) systems, focusing … Continue reading Writer Adaption for Handwritten Text Recognition of Historical Documents →

Status: taken Supervisors: Marco Peer, Florian Kleber Deep-learning-based methods for writer retrieval make use of sampling local characteristics of handwriting, for example using patches extracted at SIFT keypoint locations(see Figure 1), to learn discriminative features. To compute a global page descriptor of those local embeddings, state-of-the-art methods rely on fixed aggregation functions, e.g. sum/average pooling … Continue reading Learning Aggregation Functions for Writer Retrieval →