- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR) - Niagara Falls, NY, USA (2018.8.5-2018.8.8)] 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR) - Separating Optical and Language Models Through Encoder-Decoder Strategy for Transferable Handwriting Recognition
摘要： Lack of data can be an issue when beginning a new study on historical handwritten documents. To deal with this, we propose a deep-learning based recognizer which separates the optical and the language models in order to train them separately using different resources. In this work, we present the optical encoder part of a multilingual transductive transfer learning applied to historical handwriting recognition. The optical encoder transforms the input word image into a non-latent space that depends only on the letter-n-grams: it enables it to be independent of the language. This transformation avoids embedding a language model and operating the transfer learning across languages using the same alphabet. The language decoder creates from a vector of letter-n-grams a word as a sequence of characters. Experiments show that separating optical and language model can be a solution for multilingual transfer learning.
关键词： Optical model,Language model,knowledge transfer,Handwriting recognition
更新于2025-09-23 15:22:29
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Peculiarities of Wave Surface of a SemiconductorDielectric Metamaterial
摘要： We present GyroPen, a method to reconstruct the motion path for pen-like interaction from standard built-in sensors in modern smartphones. The key idea is to reconstruct a representation of the trajectory of the phone’s corner that is touching a writing or drawing surface from the measurements obtained from the phone’s gyroscopes and accelerometers. We propose to directly use the angular trajectory for this reconstruction, which removes the necessity for accurate absolute 3-D position estimation, a task that can be difficult using low-cost accelerometers. We connect GyroPen to a handwriting recognition system and perform two proof-of-concept experiments to demonstrate that the reconstruction accuracy of GyroPen is accurate enough to be a promising approach to text entry. In a first experiment, the average novice participant (n = 10) was able to write the first word only 37 s after the starting to use GyroPen for the first time. In a second experiment, experienced users (n = 2) were able to write at the speed of 3–4 s for one English word and with a character error rate of 18%.
关键词： text recognition,Computer and information processing,gesture recognition,pattern recognition,handwriting recognition
更新于2025-09-16 10:30:52
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[IEEE 2019 International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM) - Qingdao, China (2019.9.18-2019.9.20)] 2019 International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM) - Dual-Polarized Bandpass Three-Dimensional FSS Based on Square Waveguide Structure
摘要： We present GyroPen, a method to reconstruct the motion path for pen-like interaction from standard built-in sensors in modern smartphones. The key idea is to reconstruct a representation of the trajectory of the phone’s corner that is touching a writing or drawing surface from the measurements obtained from the phone’s gyroscopes and accelerometers. We propose to directly use the angular trajectory for this reconstruction, which removes the necessity for accurate absolute 3-D position estimation, a task that can be difficult using low-cost accelerometers. We connect GyroPen to a handwriting recognition system and perform two proof-of-concept experiments to demonstrate that the reconstruction accuracy of GyroPen is accurate enough to be a promising approach to text entry. In a first experiment, the average novice participant (n = 10) was able to write the first word only 37 s after the starting to use GyroPen for the first time. In a second experiment, experienced users (n = 2) were able to write at the speed of 3–4 s for one English word and with a character error rate of 18%.
关键词： text recognition,Computer and information processing,gesture recognition,pattern recognition,handwriting recognition
更新于2025-09-16 10:30:52