DKDS: A Benchmark Dataset of Degraded Kuzushiji Documents with Seals for Detection and Binarization

• URL: http://arxiv.org/abs/2511.09117v1
• Date: Thu, 13 Nov 2025 01:33:12 GMT
• Title: DKDS: A Benchmark Dataset of Degraded Kuzushiji Documents with Seals for Detection and Binarization
• Abstract summary: Kuzushiji, a pre-modern Japanese cursive script, can currently be read and understood by only a few thousand trained experts in Japan.<n>Current Optical Character Recognition techniques fail to consider various types of noise, such as document degradation and seals.<n>We introduce the Degraded Kuzushiji Documents with Seals dataset as a new benchmark for related tasks.
• Score: 4.045683514325492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Kuzushiji, a pre-modern Japanese cursive script, can currently be read and understood by only a few thousand trained experts in Japan. With the rapid development of deep learning, researchers have begun applying Optical Character Recognition (OCR) techniques to transcribe Kuzushiji into modern Japanese. Although existing OCR methods perform well on clean pre-modern Japanese documents written in Kuzushiji, they often fail to consider various types of noise, such as document degradation and seals, which significantly affect recognition accuracy. To the best of our knowledge, no existing dataset specifically addresses these challenges. To address this gap, we introduce the Degraded Kuzushiji Documents with Seals (DKDS) dataset as a new benchmark for related tasks. We describe the dataset construction process, which required the assistance of a trained Kuzushiji expert, and define two benchmark tracks: (1) text and seal detection and (2) document binarization. For the text and seal detection track, we provide baseline results using multiple versions of the You Only Look Once (YOLO) models for detecting Kuzushiji characters and seals. For the document binarization track, we present baseline results from traditional binarization algorithms, traditional algorithms combined with K-means clustering, and Generative Adversarial Network (GAN)-based methods. The DKDS dataset and the implementation code for baseline methods are available at https://ruiyangju.github.io/DKDS.

Related papers

• TongGuOCR: A Layout-Aware and Token-Augmented OCR MLLM for Chinese Historical Documents [50.64307290680222]
  TongGuOCR is a layout-aware and token-augmented multimodal large language model (MLLM) for OCR of Chinese historical documents.<n>TongGuOCR achieves 93.76 AR and reduces NED from 10.43 to 6.15 and RO-ED from 7.53 to 3.49 relative to the best competing score for each metric.
  arXiv  Detail & Related papers  (2026-08-08T04:50:00Z)
• Restoration-Guided Kuzushiji Character Recognition Framework under Seal Interference [4.045683514325492]
  Kuzushiji was one of the most popular writing styles in pre-modern Japan and was widely used in both personal letters and official documents.<n>Recent research has focused on developing automated Kuzushiji character recognition methods.<n>However, existing methods struggle to maintain recognition accuracy under seal interference.
  arXiv  Detail & Related papers  (2026-02-22T07:58:29Z)
• Digitizing Nepal's Written Heritage: A Comprehensive HTR Pipeline for Old Nepali Manuscripts [0.3899824115379246]
  This paper presents the first end-to-end pipeline for Handwritten Text Recognition for Old Nepali.<n>We adopt a line-level transcription approach and systematically explore encoder-decoder architectures and data-centric techniques to improve recognition accuracy.
  arXiv  Detail & Related papers  (2025-12-18T22:43:15Z)
• A Survey on Deep Text Hashing: Efficient Semantic Text Retrieval with Binary Representation [69.50397417361351]
  Text hashing projects original texts into compact binary hash codes.<n>Deep text hashing has demonstrated significant advantages over traditional, data-independent hashing techniques.<n>This survey investigates current deep text hashing methods by categorizing them based on their core components.
  arXiv  Detail & Related papers  (2025-10-31T06:51:37Z)
• KAP: MLLM-assisted OCR Text Enhancement for Hybrid Retrieval in Chinese Non-Narrative Documents [0.0]
  We propose Knowledge-Aware Preprocessing (KAP), a novel framework that transforms noisy OCR outputs into retrieval-optimized text.<n>KAP adopts a two-stage approach: it first extracts text using OCR, then employs Multimodal Large Language Models to refine the output.<n> Empirical results demonstrate that KAP consistently and significantly outperforms conventional preprocessing approaches.
  arXiv  Detail & Related papers  (2025-03-11T14:01:03Z)
• Reference-Based Post-OCR Processing with LLM for Precise Diacritic Text in Historical Document Recognition [1.6941039309214678]
  We propose a method utilizing available content-focused ebooks as a reference base to correct imperfect OCR-generated text.<n>This technique generates high-precision pseudo-page-to-page labels for diacritic languages.<n>The pipeline eliminates various types of noise from aged documents and addresses issues such as missing characters, words, and disordered sequences.
  arXiv  Detail & Related papers  (2024-10-17T08:05:02Z)
• Contextual Document Embeddings [77.22328616983417]
  We propose two complementary methods for contextualized document embeddings. First, an alternative contrastive learning objective that explicitly incorporates the document neighbors into the intra-batch contextual loss. Second, a new contextual architecture that explicitly encodes neighbor document information into the encoded representation.
  arXiv  Detail & Related papers  (2024-10-03T14:33:34Z)
• Precise Zero-Shot Dense Retrieval without Relevance Labels [60.457378374671656]
  Hypothetical Document Embeddings(HyDE) is a zero-shot dense retrieval system. We show that HyDE significantly outperforms the state-of-the-art unsupervised dense retriever Contriever.
  arXiv  Detail & Related papers  (2022-12-20T18:09:52Z)
• Lexically Aware Semi-Supervised Learning for OCR Post-Correction [90.54336622024299]
  Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Previous work has demonstrated the utility of neural post-correction methods on recognition of less-well-resourced languages. We present a semi-supervised learning method that makes it possible to utilize raw images to improve performance.
  arXiv  Detail & Related papers  (2021-11-04T04:39:02Z)
• One-shot Key Information Extraction from Document with Deep Partial Graph Matching [60.48651298832829]
  Key Information Extraction (KIE) from documents improves efficiency, productivity, and security in many industrial scenarios. Existing supervised learning methods for the KIE task need to feed a large number of labeled samples and learn separate models for different types of documents. We propose a deep end-to-end trainable network for one-shot KIE using partial graph matching.
  arXiv  Detail & Related papers  (2021-09-26T07:45:53Z)
• Extract, Integrate, Compete: Towards Verification Style Reading Comprehension [66.2551168928688]
  We present a new verification style reading comprehension dataset named VGaokao from Chinese Language tests of Gaokao. To address the challenges in VGaokao, we propose a novel Extract-Integrate-Compete approach.
  arXiv  Detail & Related papers  (2021-09-11T01:34:59Z)
• HCR-Net: A deep learning based script independent handwritten character recognition network [5.8067395321424975]
  Handwritten character recognition (HCR) remains a challenging pattern recognition problem despite decades of research. We have proposed a script independent deep learning network for HCR research, called HCR-Net, that sets a new research direction for the field.
  arXiv  Detail & Related papers  (2021-08-15T05:48:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.