Status: available Supervisors: Marco Peer The digitization and preservation of historical documents rely on accurate transcription of handwritten text. However, historical documents often present unique challenges due to variations in writing styles and deteriorated conditions.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Text Recognition (HTR) systems, focusing … Continue reading Writer Adaption for Handwritten Text Recognition of Historical Documents →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: available 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 →