Bookmarks tagged training and ai

Abstract. Transcription of User Interface (UI) elements hand drawings
to the computer code is a tedious and repetitive task. Therefore, a need arose to create a system capable of automating such process. This paper describes a deep learning-based method for hand-drawn user interface elements detection and localization. The proposed method scored 1st place in the ImageCLEFdrawnUI competition while achieving an overall precision of 0.9708. The final method is based on Faster R-CNN object detector framework with ResNet-50 backbone architecture trained with advanced regularization techniques. The code has been made available at: https://github.com/picekl/ImageCLEF2020-DrawnUI.

"We address the problem of offline handwritten diagram recognition. Recently, it has been shown that diagram symbols can be directly recognized with deep learning object detectors. However, object detectors are not able to recognize the diagram structure. We propose Arrow R-CNN, the first deep learning system for joint symbol and structure recognition in handwritten diagrams. Arrow R-CNN extends the Faster R-CNN object detector with an arrow head and tail keypoint predictor and a diagram-aware postprocessing method. We propose a network architecture and data augmentation methods targeted at small diagram datasets. Our diagram-aware postprocessing method addresses the insufficiencies of standard Faster R-CNN postprocessing. It reconstructs a diagram from a set of symbol detections and arrow keypoints. Arrow R-CNN improves state-of-the-art substantially: on a scanned flowchart dataset, we increase the rate of recognized diagrams from 37.7 to 78.6%."

"A guide for AI entrepreneurs on how to prepare a dataset for a machine learning project."

Obtaining Information From Technical Drawings Using TensorFlow, Keras-OCR and OpenCV