# Impact of Iterative Fine-Tuning on Transcription Accuracy in Complex Historical Sanskrit Manuscripts

arXiv: https://arxiv.org/abs/2608.18696 · HTML: https://mlsanity.com/paper/2608.18696/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 66 references that are in the map.
- First posted on arXiv 19 Aug 2026.

## Builds on (most cited first, 30 of 30)

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- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
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- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [PaLI-X: On Scaling up a Multilingual Vision and Language Model](https://mlsanity.com/paper/2305.18565/) (2023)
- [DeepSeek-OCR: Contexts Optical Compression](https://mlsanity.com/paper/2510.18234/) (2025)
- [SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels](https://mlsanity.com/paper/1711.08920/) (2017)
- [OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations](https://mlsanity.com/paper/2412.07626/) (2024)
- [olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models](https://mlsanity.com/paper/2502.18443/) (2025)
- [PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model](https://mlsanity.com/paper/2510.14528/) (2025)
- [DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition](https://mlsanity.com/paper/2203.12273/) (2022)
- [dhSegment: A generic deep-learning approach for document segmentation](https://mlsanity.com/paper/1804.10371/) (2018)
- [Page Layout Analysis System for Unconstrained Historic Documents](https://mlsanity.com/paper/2102.11838/) (2021)
- [READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents](https://mlsanity.com/paper/1705.03311/) (2017)
- [DocTr: Document Transformer for Structured Information Extraction in Documents](https://mlsanity.com/paper/2307.07929/) (2023)
- [Quantifying the Scanner-Induced Domain Gap in Mitosis Detection](https://mlsanity.com/paper/2103.16515/) (2021)
- [KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning](https://mlsanity.com/paper/1910.09433/) (2019)
- [Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models](https://mlsanity.com/paper/2506.20168/) (2025)
- [Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks](https://mlsanity.com/paper/2012.14163/) (2020)
- [AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions](https://mlsanity.com/paper/2104.13037/) (2021)
- [TS-Net: OCR Trained to Switch Between Text Transcription Styles](https://mlsanity.com/paper/2103.05489/) (2021)
- [Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods](https://mlsanity.com/paper/2203.12346/) (2022)
- [ICDAR 2025 Competition on FEw-Shot Text line segmentation of ancient handwritten documents (FEST)](https://mlsanity.com/paper/2509.12965/) (2025)
- [Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts](https://mlsanity.com/paper/2108.09436/) (2021)
- [GutenOCR: A Grounded Vision-Language Front-End for Documents](https://mlsanity.com/paper/2601.14490/) (2026)
- [IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing](https://mlsanity.com/paper/2512.20236/) (2025)
- [EpiSAM: Character Segmentation in Challenging Stone Inscriptions](https://mlsanity.com/paper/2606.28859/) (2026)

## Abstract (arXiv)

Digitizing the text from handwritten historical manuscripts is required to make them easily accessible, preservable, and to enable historical scholars to study them in new ways. Historical manuscripts, however, often exhibit complex heterogeneous layouts and non-standard appearance due to period-specific writing styles, page textures, camera noise, and other nuisance factors, making them difficult to perform OCR on. To tackle this challenge, we introduce a local traditional OCR pipeline, which can be iteratively fine-tuned on the target manuscript at the layout-level and the appearance-level. By adapting to the target manuscript distribution, the proposed Traditional OCR pipeline makes better predictions on subsequent pages, causing iterative reduction in human annotation effort, which is expensive and time-consuming as it requires historical domain expertise. Using this pipeline, we digitize text from three complex historical Sanskrit manuscripts and introduce a dataset with granular layout-level annotations, along with Unicode annotations in the standard PAGE-XML format. We demonstrate quantitative gains due to iterative fine-tuning of the proposed traditional OCR pipeline, and also benchmark the performance of leading Multi-Modal Large Language Models on the introduced Dataset. Code and dataset are available at: https://github.com/flame-cai/gnn-synthetic-layout-historical/.