![]() ![]() The player then has 20 seconds to complete the drawing - if the computer recognizes the drawing correctly within that time, the player earns a point. In the original “Quick, Draw!” game, the player is prompted to draw an image of a certain category (dog, cow, car, etc). Importantly, since the training data comes from the game itself (where drawings can be incomplete or may not match the label), this challenge requires the development of a classifier that can effectively learn from noisy data and perform well on a manually-labeled test set from a different distribution. In order to encourage further research in this exciting field, we have launched the Kaggle "Quick, Draw!" Doodle Recognition Challenge, which tasks participants to build a better machine learning classifier for the existing “Quick, Draw!” dataset. For example the “ Quick, Draw!” game generated a dataset of 50M drawings (out of more than 1B that were drawn) which itself inspired many different new projects. The same technology that lets you digitize handwritten text can also be used to improve your drawing abilities and build virtual worlds, and represents an exciting research direction that explores the potential of handwriting as a human-computer interaction modality. While Google products like Translate, Keep and Handwriting Input use this technology to recognize handwritten text, it works for any predefined pattern for which enough training data is available. Online handwriting recognition consists of recognizing structured patterns in freeform handwritten input. Posted by Thomas Deselaers, Senior Staff Software Engineer and Jake Walker, Product Manager, Machine Perception
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