# Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition

arXiv: https://arxiv.org/abs/1702.07975 · HTML: https://mlsanity.com/paper/1702.07975/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2017.

## Built on by (most cited first, 14 of 14)

- [Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis](https://mlsanity.com/paper/2404.13686/) (2024)
- [Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study](https://mlsanity.com/paper/2112.15093/) (2021)
- [Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis](https://mlsanity.com/paper/2507.18569/) (2025)
- [Online Multi-Granularity Distillation for GAN Compression](https://mlsanity.com/paper/2108.06908/) (2021)
- [Chinese Character Recognition with Radical-Structured Stroke Trees](https://mlsanity.com/paper/2211.13518/) (2022)
- [HierCode: A Lightweight Hierarchical Codebook for Zero-shot Chinese Text Recognition](https://mlsanity.com/paper/2403.13761/) (2024)
- [MegaHan97K: A Large-Scale Dataset for Mega-Category Chinese Character Recognition with over 97K Categories](https://mlsanity.com/paper/2506.04807/) (2025)
- [Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration](https://mlsanity.com/paper/2307.04341/) (2023)
- [Improving Chinese Character Representation with Formation Tree](https://mlsanity.com/paper/2404.12693/) (2024)
- [SAN: Structure-Aware Network for Complex and Long-tailed Chinese Text Recognition](https://mlsanity.com/paper/2411.06381/) (2024)
- [DTNN: Energy-efficient Inference with Dendrite Tree Inspired Neural Networks for Edge Vision Applications](https://mlsanity.com/paper/2105.11848/) (2021)
- [Multi-Objective Evolutionary for Object Detection Mobile Architectures Search](https://mlsanity.com/paper/2211.02791/) (2022)
- [Efficient Image Restoration through Low-Rank Adaptation and Stable Diffusion XL](https://mlsanity.com/paper/2408.17060/) (2024)
- [Entropy-Aware Structural Alignment for Zero-Shot Handwritten Chinese Character Recognition](https://mlsanity.com/paper/2602.03913/) (2026)

## Abstract (arXiv)

Like other problems in computer vision, offline handwritten Chinese character recognition (HCCR) has achieved impressive results using convolutional neural network (CNN)-based methods. However, larger and deeper networks are needed to deliver state-of-the-art results in this domain. Such networks intuitively appear to incur high computational cost, and require the storage of a large number of parameters, which renders them unfeasible for deployment in portable devices. To solve this problem, we propose a Global Supervised Low-rank Expansion (GSLRE) method and an Adaptive Drop-weight (ADW) technique to solve the problems of speed and storage capacity. 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. In tests, the proposed algorithm surpassed the best single-network performance reported thus far in the literature while requiring only 2.3 MB for storage. Furthermore, when integrated with our effective forward implementation, the recognition of an offline character image took only 9.7 ms on a CPU. Compared with the state-of-the-art CNN model for HCCR, our approach is approximately 30 times faster, yet 10 times more cost efficient.