Offline Handwritten Chinese Character Recognition
2 papers with code • 0 benchmarks • 1 datasets
Handwritten Chinese characters recognition is the task of detecting and interpreting the components of Chinese characters (i.e. radicals and two-dimensional structures).
Benchmarks
These leaderboards are used to track progress in Offline Handwritten Chinese Character Recognition
CASIA-HWDB
Latest papers with no code
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.
Deep Template Matching for Offline Handwritten Chinese Character Recognition
Just like its remarkable achievements in many computer vision tasks, the convolutional neural networks (CNN) provide an end-to-end solution in handwritten Chinese character recognition (HCCR) with great success.
DenseRAN for Offline Handwritten Chinese Character Recognition
Recently, great success has been achieved in offline handwritten Chinese character recognition by using deep learning methods.
Building Efficient CNN Architecture for Offline Handwritten Chinese Character Recognition
Deep convolutional networks based methods have brought great breakthrough in images classification, which provides an end-to-end solution for handwritten Chinese character recognition(HCCR) problem through learning discriminative features automatically.
Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition
We design a nine-layer CNN for HCCR consisting of 3, 755 classes, and devise an algorithm that can reduce the networks computational cost by nine times and compress the network to 1/18 of the original size of the baseline model, with only a 0. 21% drop in accuracy.
Online and Offline Handwritten Chinese Character Recognition: A Comprehensive Study and New Benchmark
Furthermore, although directMap+convNet can achieve the best results and surpass human-level performance, we show that writer adaptation in this case is still effective.
