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Found 14 papers, 2 papers with code
MathWriting: A Dataset For Handwritten Mathematical Expression Recognition
We introduce MathWriting, the largest online handwritten mathematical expression dataset to date.
Representing Online Handwriting for Recognition in Large Vision-Language Models
In this paper, we study online handwriting recognition with VLMs, going beyond naive OCR.
InkSight: Offline-to-Online Handwriting Conversion by Learning to Read and Write
Our work, InkSight, aims to bridge the gap by empowering physical note-takers to effortlessly convert their work (offline handwriting) to digital ink (online handwriting), a process we refer to as Derendering.
DSS: Synthesizing long Digital Ink using Data augmentation, Style encoding and Split generation
As text generative models can give increasingly long answers, we tackle the problem of synthesizing long text in digital ink.
Character Queries: A Transformer-based Approach to On-Line Handwritten Character Segmentation
On-line handwritten character segmentation is often associated with handwriting recognition and even though recognition models include mechanisms to locate relevant positions during the recognition process, it is typically insufficient to produce a precise segmentation.
Sampling and Ranking for Digital Ink Generation on a tight computational budget
We use and compare the effect of multiple sampling and ranking techniques, in the first ablation study of its kind in the digital ink domain.
Inkorrect: Online Handwriting Spelling Correction
We show that Inkorrect's Pareto frontier dominates the points that correspond to prior work.
Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking
Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with.
Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking
Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with.
WILDTRACK: A Multi-Camera HD Dataset for Dense Unscripted Pedestrian Detection
People detection methods are highly sensitive to occlusions between pedestrians, which are extremely frequent in many situations where cameras have to be mounted at a limited height.
Non-Markovian Globally Consistent Multi-Object Tracking
Many state-of-the-art approaches to multi-object tracking rely on detecting them in each frame independently, grouping detections into short but reliable trajectory segments, and then further grouping them into full trajectories.
The WILDTRACK Multi-Camera Person Dataset
People detection methods are highly sensitive to the perpetual occlusions among the targets.
Globally Consistent Multi-People Tracking using Motion Patterns
Many state-of-the-art approaches to people tracking rely on detecting them in each frame independently, grouping detections into short but reliable trajectory segments, and then further grouping them into full trajectories.
What Players do with the Ball: A Physically Constrained Interaction Modeling
Tracking the ball is critical for video-based analysis of team sports.
