arXiv:2012.12412
공식 코드 · Papers with Code
Optical Braille Recognition Using Object Detection CNN
원문 초록
This paper proposes an optical Braille recognition method that uses an object detection convolutional neural network to detect whole Braille characters at once. The proposed algorithm is robust to the deformation of the page shown in the image and perspective distortions. It makes it usable for recognition of Braille texts being shoot on a smartphone camera, including bowed pages and perspective distorted images. The proposed algorithm shows high performance and accuracy compared to existing methods. We also introduce a new "Angelina Braille Images Dataset" containing 240 annotated photos of Braille texts. The proposed algorithm and dataset are available at GitHub.
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Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection
Multiple Object Stitching for Unsupervised Representation Learning
Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study
Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
Multispectral Detection Transformer with Infrared-Centric Sensor Fusion
A Real-Time System for Egocentric Hand-Object Interaction Detection in Industrial Domains
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