4 citations · 10 across the 18 of their papers we have counts for
6 papers · 1 filter
DGME-T: Directional Grid Motion Encoding for Transformer-Based Historical Camera Movement Classification
Tingyu Lin, Armin Dadras, Florian Kleber +1
Camera movement classification (CMC) models trained on contemporary, high-quality footage often degrade when applied to archival film, where noise, missing frames, and low contrast…
ClapperText: A Benchmark for Text Recognition in Low-Resource Archival Documents
Tingyu Lin, Marco Peer, Florian Kleber +1
This paper presents ClapperText, a benchmark dataset for handwritten and printed text recognition in visually degraded and low-resource settings. The dataset is derived from 127 Wo…
Camera Movement Classification in Historical Footage: A Comparative Study of Deep Video Models
Tingyu Lin, Armin Dadras, Florian Kleber +1
Camera movement conveys spatial and narrative information essential for understanding video content. While recent camera movement classification (CMC) methods perform well on moder…
Few-Shot Connectivity-Aware Text Line Segmentation in Historical Documents
Rafael Sterzinger, Tingyu Lin, Robert Sablatnig
A foundational task for the digital analysis of documents is text line segmentation. However, automating this process with deep learning models is challenging because it requires l…
Few-Shot Segmentation of Historical Maps via Linear Probing of Vision Foundation Models
Rafael Sterzinger, Marco Peer, Robert Sablatnig
As rich sources of history, maps provide crucial insights into historical changes, yet their diverse visual representations and limited annotated data pose significant challenges f…
Towards the Influence of Text Quantity on Writer Retrieval
Marco Peer, Robert Sablatnig, Florian Kleber
This paper investigates the task of writer retrieval, which identifies documents authored by the same individual within a dataset based on handwriting similarities. While existing…