146 research outputs found
Template-Instance Loss for Offline Handwritten Chinese Character Recognition
The long-standing challenges for offline handwritten Chinese character
recognition (HCCR) are twofold: Chinese characters can be very diverse and
complicated while similarly looking, and cursive handwriting (due to increased
writing speed and infrequent pen lifting) makes strokes and even characters
connected together in a flowing manner. In this paper, we propose the template
and instance loss functions for the relevant machine learning tasks in offline
handwritten Chinese character recognition. First, the character template is
designed to deal with the intrinsic similarities among Chinese characters.
Second, the instance loss can reduce category variance according to
classification difficulty, giving a large penalty to the outlier instance of
handwritten Chinese character. Trained with the new loss functions using our
deep network architecture HCCR14Layer model consisting of simple layers, our
extensive experiments show that it yields state-of-the-art performance and
beyond for offline HCCR.Comment: Accepted by ICDAR 201
Disentangling Writer and Character Styles for Handwriting Generation
Training machines to synthesize diverse handwritings is an intriguing task.
Recently, RNN-based methods have been proposed to generate stylized online
Chinese characters. However, these methods mainly focus on capturing a person's
overall writing style, neglecting subtle style inconsistencies between
characters written by the same person. For example, while a person's
handwriting typically exhibits general uniformity (e.g., glyph slant and aspect
ratios), there are still small style variations in finer details (e.g., stroke
length and curvature) of characters. In light of this, we propose to
disentangle the style representations at both writer and character levels from
individual handwritings to synthesize realistic stylized online handwritten
characters. Specifically, we present the style-disentangled Transformer (SDT),
which employs two complementary contrastive objectives to extract the style
commonalities of reference samples and capture the detailed style patterns of
each sample, respectively. Extensive experiments on various language scripts
demonstrate the effectiveness of SDT. Notably, our empirical findings reveal
that the two learned style representations provide information at different
frequency magnitudes, underscoring the importance of separate style extraction.
Our source code is public at: https://github.com/dailenson/SDT.Comment: accepted by CVPR 2023. Source code: https://github.com/dailenson/SD
Radical Recognition in Off-Line Handwritten Chinese Characters Using Non-Negative Matrix Factorization
In the past decade, handwritten Chinese character recognition has received renewed interest with the emergence of touch screen devices. Other popular applications include on-line Chinese character dictionary look-up and visual translation in mobile phone applications. Due to the complex structure of Chinese characters, this classification task is not exactly an easy one, as it involves knowledge from mathematics, computer science, and linguistics.
Given a large image database of handwritten character data, the goal of my senior project is to use Non-Negative Matrix Factorization (NMF), a recent method for finding a suitable representation (parts-based representation) of image data, to detect specific sub-components in Chinese characters. NMF has only been applied to typed (printed) Chinese characters in different fonts. This project focuses specifically on how well NMF works on handwritten characters. In addition, research in Chinese character classification has mainly been done using holistic approaches - treating each character as an inseparable unit. By using NMF, this project takes a different approach by focusing on a more specific problem in Chinese character classification: radical (sub-component) detection.
Finally, a possible application of radical detection will be proposed. This interactive application can potentially help Chinese language learners better recognize characters by radicals
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