The York Urban Line Segment Database is a compilation of 102 images (45 indoor, 57 outdoor) of urban environments consisting mostly of scenes from the campus of York University and downtown Toronto, Canada Each image in the database has been hand-labelled to identify the set of line segments satisfying the “Manhattan assumption” (Coughlan & Yuille 2003), i.e., the set of line segments that conform to the The database provides the original images, camera calibration parameters, ground truth line segments, and estimated Manhattan frame relative to the camera for each image.
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…It can be applied in multiple tasks, such as object detection, instance segmentation, semantic segmentation, free-space segmentation, and waterline segmentation.
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…We also provided the bounding box annotations (YOLO format) for the segmentation of words/lines and the ground truth annotations for full-text, along with the segmented images and their positions. The BN-HTRd dataset can be adopted as a basis for various handwriting classification tasks such as end-to-end document recognition, word-spotting, word/line segmentation, and so on.
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…Géraud Official competition website: https://icdar21-mapseg.github.io/ This is the dataset of the ICDAR 2021 Competition on Historical Map Segmentation (“MapSeg”). The general pipeline involves multiples stages; we list some essential ones here: segment map content: locate the area of the image which contains map content; extract map object from different layers Task 2: “Segment Map Area” This tasks is the equivalent of text area detection for OCR: given the image of a complete map sheet, you need to segment the area which contains map content. We decided to segment each of those regions as closely as possible. Expected output for this task is a binary mask indicating for each pixel whether it belongs to the map area or not.
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