# DropSample: A New Training Method to Enhance Deep Convolutional Neural Networks for Large-Scale Unconstrained Handwritten Chinese Character Recognition

arXiv: https://arxiv.org/abs/1505.05354 · HTML: https://mlsanity.com/paper/1505.05354/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2015.

## Built on by (most cited first, 12 of 12)

- [Recognition of Handwritten Chinese Text by Segmentation: A Segment-annotation-free Approach](https://mlsanity.com/paper/2207.14801/) (2022)
- [Physics-constrained Deep Learning for Robust Inverse ECG Modeling](https://mlsanity.com/paper/2107.12780/) (2021)
- [RZCR: Zero-shot Character Recognition via Radical-based Reasoning](https://mlsanity.com/paper/2207.05842/) (2022)
- [Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition](https://mlsanity.com/paper/2110.13008/) (2021)
- [Toward Zero-shot Character Recognition: A Gold Standard Dataset with Radical-level Annotations](https://mlsanity.com/paper/2308.00655/) (2023)
- [Progressive Data Dropout: An Embarrassingly Simple Approach to Faster Training](https://mlsanity.com/paper/2505.22342/) (2025)
- [Recognition of Handwritten Japanese Characters Using Ensemble of Convolutional Neural Networks](https://mlsanity.com/paper/2306.03954/) (2023)
- [Signature Isolation Forest](https://mlsanity.com/paper/2403.04405/) (2024)
- [Handwritten Digit Recognition: An Ensemble-Based Approach for Superior Performance](https://mlsanity.com/paper/2503.06104/) (2025)
- [Zero-Shot Chinese Character Recognition with Hierarchical Multi-Granularity Image-Text Aligning](https://mlsanity.com/paper/2505.24837/) (2025)
- [Sentence-level Online Handwritten Chinese Character Recognition](https://mlsanity.com/paper/2108.02561/) (2021)
- [Logographic Character Visual Pretraining via Semantic-based Contrastive Learning](https://mlsanity.com/paper/2608.00096/) (2026)

## Abstract (arXiv)

Inspired by the theory of Leitners learning box from the field of psychology, we propose DropSample, a new method for training deep convolutional neural networks (DCNNs), and apply it to large-scale online handwritten Chinese character recognition (HCCR). According to the principle of DropSample, each training sample is associated with a quota function that is dynamically adjusted on the basis of the classification confidence given by the DCNN softmax output. After a learning iteration, samples with low confidence will have a higher probability of being selected as training data in the next iteration; in contrast, well-trained and well-recognized samples with very high confidence will have a lower probability of being involved in the next training iteration and can be gradually eliminated. As a result, the learning process becomes more efficient as it progresses. Furthermore, we investigate the use of domain-specific knowledge to enhance the performance of DCNN by adding a domain knowledge layer before the traditional CNN. By adopting DropSample together with different types of domain-specific knowledge, the accuracy of HCCR can be improved efficiently. Experiments on the CASIA-OLHDWB 1.0, CASIA-OLHWDB 1.1, and ICDAR 2013 online HCCR competition datasets yield outstanding recognition rates of 97.33%, 97.06%, and 97.51% respectively, all of which are significantly better than the previous best results reported in the literature.