Line Segment Detection

The Wireframe dataset consists of 5,462 images (5,000 for training, 462 for test) of indoor and outdoor man-made scenes. Source: MCMLSD: A Probabilistic Algorithm and Evaluation Framework for Line Segment Detection Image Source: https://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Learning_to_Parse_CVPR_2018_paper.pdf

This data set provides Light Detection and Ranging (LiDAR) data and stereo image with various position sensors targeting a highly complex urban environment. The presented data set captures features in urban environments (e.g. metropolis areas, complex buildings and residential areas). The data of 2D and 3D LiDAR are provided, which are typical types of LiDAR sensors. Raw sensor data for vehicle navigation is presented in a file format. For convenience, development tools are provided in the Robot Operating System (ROS) environment. Source: https://sites.google.com/view/complex-urban-dataset Image Source: https://irap.kaist.ac.kr/dataset/

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. The images are 640 x 480 in size and have been taken with a calibrated Panasonic Lumix DMC-LC80 digital camera. 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 3D orthogonal frame of the urban environment. These hand-labelled data have been used to identify the three Manhattan vanishing points in each image and from these to identify the Euler angles relating the camera frame to the Manhattan frame of the scene. The database provides the original images, camera calibration parameters, ground truth line segments, and estimated Manhattan frame relative to the camera for each image. Source: York Urban Line Segment Database

A Multi-Task 4D Radar-Camera Fusion Dataset for Autonomous Driving on Water Surfaces description of the dataset * WaterScenes, the first multi-task 4D radar-camera fusion dataset on water surfaces, which offers data from multiple sensors, including a 4D radar, monocular camera, GPS, and IMU. It can be applied in multiple tasks, such as object detection, instance segmentation, semantic segmentation, free-space segmentation, and waterline segmentation. * Our dataset covers diverse time conditions (daytime, nightfall, night), lighting conditions (normal, dim, strong), weather conditions (sunny, overcast, rainy, snowy) and waterway conditions (river, lake, canal, moat). An information list is also offered for retrieving specific data for experiments under different conditions. * We provide 2D box-level and pixel-level annotations for camera images, and 3D point-level annotations for radar point clouds. We also offer precise timestamps for the synchronization of different sensors, as well as intrinsic and extrinsic parameters. * We provide a toolkit for radar point clouds that includes: pre-processing, labeling, projection and visualization, assisting researchers in processing and analyzing our dataset.

We introduce a new Dataset (BN-HTRd) for offline Handwritten Text Recognition (HTR) from images of Bangla scripts comprising words, lines, and document-level annotations. The BN-HTRd dataset is based on the BBC Bangla News corpus - which acted as ground truth texts for the handwritings. Our dataset contains a total of 786 full-page images collected from 150 different writers. With a staggering 1,08,181 instances of handwritten words, distributed over 14,383 lines and 23,115 unique words, this is currently the 'largest and most comprehensive dataset' in this field. 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 contents of our dataset came from a diverse news category, and annotators of different ages, genders, and backgrounds, having variability in writing styles. 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. The statistics of the original dataset are given below: - Number of writers = 150 - Total number of images = 786 - Total number of lines = 14,383 - Total number of words = 1,08,181 - Total number of unique words = 23,115 - Total number of punctuation = 7,446 - Total number of characters = 5,74,203 From v3.0 onwards, we are also providing automatic bounding box annotations (YOLO format) of 805 document images containing words/lines. The statistics of the automatic annotations are given below: - Number of writers = 87 - Total number of images = 805 - Total number of lines = 14,836 - Total number of words = 1,06,135

• Revision: v1.0.0-full-20210527a - DOI: 10.5281/zenodo.4817662 - Authors: J. Chazalon, E. Carlinet, Y. Chen, J. Perret, C. Mallet, B. Duménieu and T. Géraud - Official competition website: https://icdar21-mapseg.github.io/ This is the dataset of the ICDAR 2021 Competition on Historical Map Segmentation (“MapSeg”). This competition ran from November 2020 to April 2021. ## Motivation This competition aims as encouraging research in the digitization of historical maps. In order to be usable in historical studies, information contained in such images need to be extracted. 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: detect objects like roads, buildings, building blocks, rivers, etc. to create geometric data; - georeference the map: by detecting objects at known geographic coordinate, compute the transformation to turn geometric objects into geographic ones (which can be overlaid on current maps). ## Tasks The tasks we propose simulate the three essential digitization steps we just mentioned. ### Task 1: “Detect Building Blocks” This task is the flagship of this competition. Given a fragment of map sheet image focused on map content, you need to detect the building blocks. Building blocks are symbolized by a thick line. They do not overlap between themselves, but many other elements can perturb their detection: - special buildings (hatched areas) can be included in building blocks (sometimes they cover the building block completely); - text can be overlaid on lines; - those maps contain many lines which need to be filtered out (internal building structures, railways, rivers, gardens…). Expected output for this task is a binary mask indicating for each pixel whether it belongs to a building block or not. Evaluation tools also tolerate a label map in TIFF format, where each pixel is labelled with the identifier of the shape it belongs to, using an INT16. To extract shapes from the binary mask, 4-connectivity is used (hence the background has 8-connectivity). ### 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. This area is usually well separated from the other elements (title, legend, scale…) by several frames but sometimes map contents exceeds the frame for some large objects. While most of the area is delineated by straight lines, some objects were drawn outside the frame on several sheets. 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. ### Task 3: “Locate Graticule Lines Intersections” This task is essential to the georeferencing of the map: graticule lines are lines which indicate the North/South/East/West coordinates relative to the reference point. Their intersection points are very useful to provide key points for the registration of the map image. Given the image of a complete map sheet, you need to locate the intersection points of such lines. These lines usually cover the map content from left to right or from top to bottom but beware: - due to document aging paper sheets are not flat anymore and lines are not straight; - lines may be in diagonal for some areas; - lines can be overlaid with many other objects. Expected output for this task is a list of coordinates (in image referential, i.e. 0,0 at top left, x-axis pointing to the right and y-axis pointing downward).