Mind the Gap: Analyzing Lacunae with Transformer-Based Transcription

• URL: http://arxiv.org/abs/2407.00250v1
• Date: Fri, 28 Jun 2024 22:52:39 GMT
• Title: Mind the Gap: Analyzing Lacunae with Transformer-Based Transcription
• Abstract summary: We employ transformer-based optical character recognition (OCR) models trained on synthetic data containing lacunae. We demonstrate their effectiveness in detecting and restoring lacunae, achieving a success rate of 65%.
• Score: 2.486161976966064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Historical documents frequently suffer from damage and inconsistencies, including missing or illegible text resulting from issues such as holes, ink problems, and storage damage. These missing portions or gaps are referred to as lacunae. In this study, we employ transformer-based optical character recognition (OCR) models trained on synthetic data containing lacunae in a supervised manner. We demonstrate their effectiveness in detecting and restoring lacunae, achieving a success rate of 65%, compared to a base model lacking knowledge of lacunae, which achieves only 5% restoration. Additionally, we investigate the mechanistic properties of the model, such as the log probability of transcription, which can identify lacunae and other errors (e.g., mistranscriptions due to complex writing or ink issues) in line images without directly inspecting the image. This capability could be valuable for scholars seeking to distinguish images containing lacunae or errors from clean ones. Although we explore the potential of attention mechanisms in flagging lacunae and transcription errors, our findings suggest it is not a significant factor. Our work highlights a promising direction in utilizing transformer-based OCR models for restoring or analyzing damaged historical documents.

Related papers

• GenShield: Unified Detection and Artifact Correction for AI-Generated Images [65.11434977803509]
  GenShield is a framework that performs AIGI detection and artifact correction in a closed loop from diagnosis to restoration.<n>A high-quality dataset with large-scale artifact-restored'' pairs is constructed alongside a unified evaluation pipeline.
  arXiv  Detail & Related papers  (2026-05-15T16:06:20Z)
• Towards Agentic Defect Reasoning: A Graph-Assisted Retrieval Framework for Laser Powder Bed Fusion [0.0]
  This study presents a graph-assisted retrieval framework for defect reasoning in Laser Powder Bed Fusion.<n>The framework integrates semantic and graph-based retrieval, supported by a lightweight agent-based reasoning layer.<n>The framework enables transparent reasoning chains linking process parameters to defects.
  arXiv  Detail & Related papers  (2026-04-05T18:01:31Z)
• Error Patterns in Historical OCR: A Comparative Analysis of TrOCR and a Vision-Language Model [0.07874708385247352]
  OCR of eighteenth-century printed texts remains challenging due to degraded print quality, archaic glyphs, and non-standardized orthography.<n>We compare a dedicated OCR transformer (TrOCR) and a general-purpose Vision-Language Model (Qwen) on line-level historical English texts.<n>TrOCR preserves orthographic fidelity more consistently but is more prone to cascading error propagation.
  arXiv  Detail & Related papers  (2026-02-16T07:17:52Z)
• Low-Resource Heuristics for Bahnaric Optical Character Recognition Improvement [3.2537431443459255]
  Bahnar, a minority language spoken across Vietnam, Cambodia, and Laos, faces significant preservation challenges due to limited research and data availability.<n>This study addresses the critical need for accurate digitization of Bahnar language documents through optical character recognition (OCR) technology.<n>We propose a comprehensive approach combining advanced table and non-table detection techniques with probability-based post-processings to enhance recognition accuracy.
  arXiv  Detail & Related papers  (2026-01-06T12:22:03Z)
• Robustness of Structured Data Extraction from Perspectively Distorted Documents [0.0]
  Real-world document images are usually not only in-plane rotated but also perspectively distorted.<n>This study investigates the impacts of such perturbations on the data extraction accuracy for the state-of-the-art model, Gemini-1.5-pro.
  arXiv  Detail & Related papers  (2025-11-18T07:54:21Z)
• Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection [58.535473924035365]
  Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns.<n>We tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs.<n>We introduce a corruption model that injects artificial disruptions into training images to mimic structural defects.
  arXiv  Detail & Related papers  (2025-11-10T15:48:50Z)
• Seeing the Unseen: Towards Zero-Shot Inspection for Wind Turbine Blades using Knowledge-Augmented Vision Language Models [10.230967860299504]
  We propose a zero-shot-oriented inspection framework that integrates Retrieval-Augmented Generation with Vision-Language Models.<n>A multimodal knowledge base is constructed, comprising technical documentation, representative reference images, and domain-specific guidelines.<n>We evaluate the framework on 30 labeled blade images covering diverse damage categories.
  arXiv  Detail & Related papers  (2025-10-26T23:19:28Z)
• SINDER: Repairing the Singular Defects of DINOv2 [61.98878352956125]
  Vision Transformer models trained on large-scale datasets often exhibit artifacts in the patch token they extract. We propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset.
  arXiv  Detail & Related papers  (2024-07-23T20:34:23Z)
• Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection [106.39544368711427]
  We study the problem of generalizable synthetic image detection, aiming to detect forgery images from diverse generative methods. We present a novel forgery-aware adaptive transformer approach, namely FatFormer. Our approach tuned on 4-class ProGAN data attains an average of 98% accuracy to unseen GANs, and surprisingly generalizes to unseen diffusion models with 95% accuracy.
  arXiv  Detail & Related papers  (2023-12-27T17:36:32Z)
• Exposing Image Splicing Traces in Scientific Publications via Uncertainty-guided Refinement [30.698359275889363]
  A surge in scientific publications suspected of image manipulation has led to numerous retractions. Image splicing detection is more challenging due to the lack of reference images and the typically small tampered areas. We propose an Uncertainty-guided Refinement Network (URN) to mitigate the impact of disruptive factors.
  arXiv  Detail & Related papers  (2023-09-28T12:36:12Z)
• What can we learn about a generated image corrupting its latent representation? [57.1841740328509]
  We investigate the hypothesis that we can predict image quality based on its latent representation in the GANs bottleneck. We achieve this by corrupting the latent representation with noise and generating multiple outputs.
  arXiv  Detail & Related papers  (2022-10-12T14:40:32Z)
• Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors [15.842163335920954]
  We propose a model based on Retina drilling defects in X-ray images of turbine blades. The application is challenging due to the image resolutions in which defects are very small and hardly captured by the commonly used anchor sizes. We validate the model with $3$-fold cross-validation, showing a very high accuracy in identifying images with defects.
  arXiv  Detail & Related papers  (2022-08-08T18:44:51Z)
• Understanding invariance via feedforward inversion of discriminatively trained classifiers [30.23199531528357]
  Past research has discovered that some extraneous visual detail remains in the output logits. We develop a feedforward inversion model that produces remarkably high fidelity reconstructions. Our approach is based on BigGAN, with conditioning on logits instead of one-hot class labels.
  arXiv  Detail & Related papers  (2021-03-15T17:56:06Z)
• Neural OCR Post-Hoc Correction of Historical Corpora [4.427447378048202]
  We propose a neural approach based on a combination of recurrent (RNN) and deep convolutional network (ConvNet) to correct OCR transcription errors. We show that our models are robust in capturing diverse OCR transcription errors and reduce the word error rate of 32.3% by more than 89%.
  arXiv  Detail & Related papers  (2021-02-01T01:35:55Z)
• Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation [79.58311369297635]
  We propose a new weakly-supervised lesions transfer framework, which can explore transferable domain-invariant knowledge across different datasets. A Wasserstein quantified transferability framework is developed to highlight widerange transferable contextual dependencies. A novel self-supervised pseudo label generator is designed to equally provide confident pseudo pixel labels for both hard-to-transfer and easy-to-transfer target samples.
  arXiv  Detail & Related papers  (2020-12-08T02:26:03Z)
• Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets [56.38623317907416]
  We use Google Cloud to transcribe podcast episodes of an NPR radio show. We then build a pipeline for systematically pre-processing the text. Our model successfully identified that Omeprazole can help treat heartburn.
  arXiv  Detail & Related papers  (2020-10-22T19:52:49Z)
• Salvage Reusable Samples from Noisy Data for Robust Learning [70.48919625304]
  We propose a reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the networks.
  arXiv  Detail & Related papers  (2020-08-06T02:07:21Z)

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.