데이터셋 탐색

Letter Recognition Data Set is a handwritten digit dataset. The task is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts and each letter within these 20 fonts was randomly distorted to produce a file of 20,000 unique stimuli. Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15. Source: UCL Machine Learning Repository Letter Recognition Image Source: http://www.cs.uu.nl/docs/vakken/mpr/Frey-Slate.pdf

The LastLetterConcat dataset is a collection of word concatenations formed by taking the last letters of individual words and joining them together. Each entry in the dataset consists of a question and an answer, where the answer is the result of concatenating the last letters of specific words. Here are some examples: ylyo: Take the last letters of each word in “Kelly Cheryl Nancy Jojo” and concatenate them. adey: Take the last letters of each word in “Gisela Gerald Mackenzie Ashley” and concatenate them. The dataset contains a total of 500 such examples.

This large collection of over 161,000 video-label pairs of video clips, shows humans drawing letters and digits in the air, and is used to evaluate a model’s ability to classify articulated motions correctly. Unlike existing video datasets, AirLetters’ accurate classification predictions rely on discerning motion patterns and integrating information presented by the video over time (i.e., over many frames of video). That study revealed that while trivial for humans, accurate representations of complex articulated motions remain an open problem for end-to-end learning for video understanding models.

A dataset with $23\,870$ digital trajectories (i.e. time series) of handwritten lower- and uppercase Latin letters and Arabic numbers ($a$-$z$, $A$-$Z$, $0$-$9$), generated by $77$ experts using a Wacom Pen Tablet. An expert is considered a proficient user of the recorded symbols, in this case adult native German speakers. DigiLetTs was created to extend the Omniglot dataset and contains five variants per character per subject to allow the quantification of intra-subject variability and to assess and account for individual writing styles. The determination and imitation of subject-dependent writing styles is introduced as a new task in this paper. For more information about the dataset, please refer to the repository (Homepage button below).