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Found 5 papers, 1 papers with code
Linear Object Detection in Document Images using Multiple Object Tracking
Linear objects convey substantial information about document structure, but are challenging to detect accurately because of degradation (curved, erased) or decoration (doubled, dashed).
A Benchmark of Nested Named Entity Recognition Approaches in Historical Structured Documents
Our results show that while nested NER approaches enable extracting structured data directly, they do not benefit from the extra knowledge provided during training and reach a performance similar to the base approach on flat entities.
Entry Separation using a Mixed Visual and Textual Language Model: Application to 19th century French Trade Directories
When extracting structured data from repetitively organized documents, such as dictionaries, directories, or even newspapers, a key challenge is to correctly segment what constitutes the basic text regions for the target database.
ICDAR 2021 Competition on Historical Map Segmentation
Task~2 consists in segmenting map content from the larger map sheet, and was won by the UWB team using a U-Net-like FCN combined with a binarization method to increase detection edge accuracy.
Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation
Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task.
