# Character decomposition to resolve class imbalance problem in Hangul OCR

arXiv: https://arxiv.org/abs/2208.06079 · HTML: https://mlsanity.com/paper/2208.06079/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition](https://mlsanity.com/paper/2103.06495/) (2021)
- [Towards Accurate Scene Text Recognition with Semantic Reasoning Networks](https://mlsanity.com/paper/2003.12294/) (2020)
- [SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition](https://mlsanity.com/paper/2203.10209/) (2022)
- [On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention](https://mlsanity.com/paper/1910.04396/) (2019)
- [Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)](https://mlsanity.com/paper/2001.00139/) (2020)
- [GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition](https://mlsanity.com/paper/2002.01276/) (2020)
- [Reading Scene Text in Deep Convolutional Sequences](https://mlsanity.com/paper/1506.04395/) (2015)
- [SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models](https://mlsanity.com/paper/2107.09313/) (2021)
- [Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition](https://mlsanity.com/paper/2106.11613/) (2021)
- [DenseRAN for Offline Handwritten Chinese Character Recognition](https://mlsanity.com/paper/1808.04134/) (2018)
- [A Holistic Representation Guided Attention Network for Scene Text Recognition](https://mlsanity.com/paper/1904.01375/) (2019)

## Built on by (most cited first, 1 of 1)

- [Developing an OCR model for Extracting Information from Invoices with Korean Language](https://mlsanity.com/paper/2609.35796/) (2026)

## Abstract (arXiv)

We present a novel approach to OCR(Optical Character Recognition) of Korean character, Hangul. As a phonogram, Hangul can represent 11,172 different characters with only 52 graphemes, by describing each character with a combination of the graphemes. As the total number of the characters could overwhelm the capacity of a neural network, the existing OCR encoding methods pre-define a smaller set of characters that are frequently used. This design choice naturally compromises the performance on long-tailed characters in the distribution. In this work, we demonstrate that grapheme encoding is not only efficient but also performant for Hangul OCR. Benchmark tests show that our approach resolves two main problems of Hangul OCR: class imbalance and target class selection.