# Multi-Expert Routing for Multi-Domain Low-Resource OCR: A Manchu Case Study

arXiv: https://arxiv.org/abs/2607.14041 · HTML: https://mlsanity.com/paper/2607.14041/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Expert routing strategies, one of 12 directions in Mixture-of-experts.
- Builds on 29 of its 44 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Placed in

- Mixture-of-experts / [Expert routing strategies](https://mlsanity.com/moe/expert-routing-strategies/)

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models](https://mlsanity.com/paper/2401.06066/) (2024)
- [Scaling Vision with Sparse Mixture of Experts](https://mlsanity.com/paper/2106.05974/) (2021)
- [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)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://mlsanity.com/paper/2210.03347/) (2022)
- [Snapshot Ensembles: Train 1, get M for free](https://mlsanity.com/paper/1704.00109/) (2017)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [Nougat: Neural Optical Understanding for Academic Documents](https://mlsanity.com/paper/2308.13418/) (2023)
- [General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model](https://mlsanity.com/paper/2409.01704/) (2024)
- [KOSMOS-2.5: A Multimodal Literate Model](https://mlsanity.com/paper/2309.11419/) (2023)
- [A Survey of Historical Document Image Datasets](https://mlsanity.com/paper/2203.08504/) (2022)
- [Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition](https://mlsanity.com/paper/2107.10064/) (2021)
- [Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)](https://mlsanity.com/paper/2010.14476/) (2020)
- [Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts](https://mlsanity.com/paper/1912.07025/) (2019)
- [QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large Language Model Adaptation](https://mlsanity.com/paper/2506.02295/) (2025)
- [Finetuning Vision-Language Models as OCR Systems for Low-Resource Languages: A Case Study of Manchu](https://mlsanity.com/paper/2507.06761/) (2025)
- [Ancient but Digitized: Developing Handwritten Optical Character Recognition for East Syriac Script Through Creating KHAMIS Dataset](https://mlsanity.com/paper/2408.13631/) (2024)
- [Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques](https://mlsanity.com/paper/2507.06275/) (2025)
- [OmniOCR: Generalist OCR for Ethnic Minority Languages](https://mlsanity.com/paper/2602.21042/) (2026)
- [GlotOCR Bench: OCR Models Still Struggle Beyond a Handful of Unicode Scripts](https://mlsanity.com/paper/2604.12978/) (2026)
- [A Text Recognition Dataset from Sahidic Coptic Ancient Manuscripts](https://mlsanity.com/paper/2606.15987/) (2026)
- [Automated sign detection across the Electronic Babylonian Library: A large-scale dataset and end-to-end cuneiform OCR pipeline](https://mlsanity.com/paper/2606.22608/) (2026)

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

- [Collapse, Not Complexity: Failure-Conditioned Decomposition Repair for End-to-End Document Parsing](https://mlsanity.com/paper/2609.23592/) (2026)

## Abstract (arXiv)

Historical Manchu OCR must accommodate various visually distinct writing styles, including regular script, running script, and the semi-cursive chancery hand used in palace memorials, despite limited labeled data. We study a multi-expert system that reuses checkpoints from an iterative fine-tuning process as domain specialists and uses a lightweight page-level image classifier to dispatch pages by visual style. When the checkpoint pool lacks a suitable specialist, we train an additional expert for that domain. On three frozen test sets, the routed system matches the selected specialist for each style at two-decimal precision: 0.30 percent CER on regular script, 1.57 percent on memorials, and 4.83 percent on running script. The router achieves 99.3 percent page-level domain accuracy and matches the domain-label oracle at the same precision. Two of the three selected specialists were not trained specifically for their final domain; only the running-script expert was trained with that domain as its target. We report the evaluation protocol, router design, and per-page predictions to make the comparison reproducible.