# Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with Radical Reconstruction

arXiv: https://arxiv.org/abs/2406.03019 · HTML: https://mlsanity.com/paper/2406.03019/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [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)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020)
- [SPTS v2: Single-Point Scene Text Spotting](https://mlsanity.com/paper/2301.01635/) (2023)
- [SPTS: Single-Point Text Spotting](https://mlsanity.com/paper/2112.07917/) (2021)
- [DenseRAN for Offline Handwritten Chinese Character Recognition](https://mlsanity.com/paper/1808.04134/) (2018)
- [An open dataset for oracle bone script recognition and decipherment](https://mlsanity.com/paper/2401.15365/) (2024)
- [An open dataset for the evolution of oracle bone characters: EVOBC](https://mlsanity.com/paper/2401.12467/) (2024)
- [Unsupervised Structure-Texture Separation Network for Oracle Character Recognition](https://mlsanity.com/paper/2205.06549/) (2022)
- [Toward Zero-shot Character Recognition: A Gold Standard Dataset with Radical-level Annotations](https://mlsanity.com/paper/2308.00655/) (2023)

## Built on by (most cited first, 8 of 8)

- [MCCD: A Multi-Attribute Chinese Calligraphy Character Dataset Annotated with Script Styles, Dynasties, and Calligraphers](https://mlsanity.com/paper/2507.06948/) (2025)
- [Interpretable Oracle Bone Script Decipherment through Radical and Pictographic Analysis with LVLMs](https://mlsanity.com/paper/2508.10113/) (2025)
- [A Cross-Font Image Retrieval Network for Recognizing Undeciphered Oracle Bone Inscriptions](https://mlsanity.com/paper/2409.06381/) (2024)
- [Specializing Large Models for Oracle Bone Script Interpretation via Component-Grounded Multimodal Knowledge Augmentation](https://mlsanity.com/paper/2604.06711/) (2026)
- [Towards Computational Chinese Paleography](https://mlsanity.com/paper/2601.06753/) (2026)
- [OracleAnalyser: Analysing Implicit Semantics of Oracle Bone Scripts through MLLMs with Post-training](https://mlsanity.com/paper/2606.25906/) (2026)
- [AlphaOracle: Oracle bone script decipherment via human-workflow-inspired deep learning](https://mlsanity.com/paper/2607.17849/) (2026)
- [JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesis](https://mlsanity.com/paper/2608.11741/) (2026)

## Abstract (arXiv)

Oracle Bone Inscriptions is one of the oldest existing forms of writing in the world. However, due to the great antiquity of the era, a large number of Oracle Bone Inscriptions (OBI) remain undeciphered, making it one of the global challenges in the field of paleography today. This paper introduces a novel approach, namely Puzzle Pieces Picker (P3), to decipher these enigmatic characters through radical reconstruction. We deconstruct OBI into foundational strokes and radicals, then employ a Transformer model to reconstruct them into their modern (conterpart)textcolorbluecounterparts, offering a groundbreaking solution to ancient script analysis. To further this endeavor, a new Ancient Chinese Character Puzzles (ACCP) dataset was developed, comprising an extensive collection of character images from seven key historical stages, annotated with detailed radical sequences. The experiments have showcased considerable promising insights, underscoring the potential and effectiveness of our approach in deciphering the intricacies of ancient Chinese scripts. Through this novel dataset and methodology, we aim to bridge the gap between traditional philology and modern document analysis techniques, offering new insights into the rich history of Chinese linguistic heritage.