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ShareScore release 0.9.0
Dataset results
5 results for “Baseline Detection”
ScriptNet: ICDAR 2017 Competition on Baseline Detection in Archival Documents (cBAD)
<p>This dataset contains the training and test set for the ICDAR 2017 Competition on Baseline Detection in Archival Documents (cBAD).</p> <p>A newly created freely available real world dataset consisting of 2035 annotated document page images that are collected from 9 different archives and form the basis of cBAD. Two competition tracks test different characteristics of the methods submitted. Track A [Simple Documents] is published with annotated text regions and tests therefore a method's quality of text line segmentation. The more challenging Track B [Complex Documents] provides only the page area. Hence, baseline detection algorithms need to correctly locate text lines in the presence of marginalia, tables, and noise.</p> <p>The dataset comprises images with additional PAGE XMLs. The PAGE XMLs contain text regions and baseline annotations.</p> <p>Competition Website: https://scriptnet.iit.demokritos.gr/competitions/5/</p> <p>Version 3 is the version of the cBad competition</p> <p>Version 4 contains also the page region and in case of a double-page the page split as separator.</p>
ICDAR 2019 Competition on Baseline Detection (cBAD)
<p>This dataset contains the training, evaluation, and test set for the ICDAR 2019 Competition on Baseline Detection (cBAD).</p> <p>A newly created freely available real world dataset consisting of 3021 annotated document page images that are collected from seven European archives and form the basis of cBAD. The baselines in all images were manually annotated. The training and the evaluation sets contain PAGE XMLs with annotated text regions and baselines.</p> <p>Competition Website: https://scriptnet.iit.demokritos.gr/competitions/11/</p>
Brainport, Urban driving, baseline test, camera detection
<p><strong>Scenario description</strong>:</p> <p>Base line test: no CEMA, no GeoFencing enabled. Vehicle only brakes on camera detection, when vehicle is blocked on its route by a crowd</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_EAI2Mobile</strong>: Data from the service to the mobile</p> <p>Dataset Description This dataset contains information sent to the mobile about the Estimated Arrival time and position</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_CEMA_Message</strong>: Data from the service to the vehicle</p> <p>Dataset Description This dataset contains information from the Crowd Estimation and Mobility Analytics service</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_FlowRadar_Message</strong>: Data from the vehicle to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_IOT_VehicleStatus</strong>: Data sent from the vehicle to the service</p> <p>Dataset Description This dataset contains the current status of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_SmartphoneGPS</strong>: Data sent by the mobile to the service</p> <p>Dataset Description This dataset contains the GPS informaton (speed,position,heading) from the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_SmartphoneStatus</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the current status of the mobile</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_TaxiRequest</strong>: Data sent from the mobile to the service</p> <p>Dataset Description This dataset contains the requests for a taxi from the mobile phones</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_UrbanDriving_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Baseline Detection in Arabic-script Manuscripts Dataset (BADAM)
<p>A freely licensed dataset of 400 annotated Arabic-script manuscript pages for baseline detection. Refer to the <a href="https://hal.archives-ouvertes.fr/hal-02167164">article</a> for more information.</p> <p> </p> <p> </p>
Thai depression detection dataset and baseline models
<p>Depression detection dataset and baseline models. If you are only interested in the dataset, download data.zip.</p> <p>Please cite: Hämäläinen, M., Patpong, P., Alnajjar, K., Partanen, N. & Rueter, J. (2021) Detecting Depression in Thai Blog Posts: a Dataset and a Baseline. In <em>Proceedings of the 7th Workshop on Noisy User-generated Text (W-NUT 2021)</em>.</p>
ScienceDex guides
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International Brain Laboratory public data
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OpenNeuro
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