OmniOCR: Generalist OCR for Ethnic Minority Languages

• URL: http://arxiv.org/abs/2602.21042v1
• Date: Tue, 24 Feb 2026 16:02:49 GMT
• Title: OmniOCR: Generalist OCR for Ethnic Minority Languages
• Abstract summary: OmniOCR is a universal framework for ethnic minority scripts.<n>Dynamic Low-Rank Adaptation (Dynamic LoRA) allocates model capacity across layers and scripts.<n>A sparsity regularization prunes redundant updates, ensuring compact and efficient adaptation without extra inference cost.
• Score: 7.516228929594496
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Optical character recognition (OCR) has advanced rapidly with deep learning and multimodal models, yet most methods focus on well-resourced scripts such as Latin and Chinese. Ethnic minority languages remain underexplored due to complex writing systems, scarce annotations, and diverse historical and modern forms, making generalization in low-resource or zero-shot settings challenging. To address these challenges, we present OmniOCR, a universal framework for ethnic minority scripts. OmniOCR introduces Dynamic Low-Rank Adaptation (Dynamic LoRA) to allocate model capacity across layers and scripts, enabling effective adaptation while preserving knowledge.A sparsity regularization prunes redundant updates, ensuring compact and efficient adaptation without extra inference cost. Evaluations on TibetanMNIST, Shui, ancient Yi, and Dongba show that OmniOCR outperforms zero-shot foundation models and standard post training, achieving state-of-the-art accuracy with superior parameter efficiency, and compared with the state-of-the-art baseline models, it improves accuracy by 39%-66% on these four datasets. Code: https://github.com/AIGeeksGroup/OmniOCR.

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• TongGuOCR: A Layout-Aware and Token-Augmented OCR MLLM for Chinese Historical Documents [50.64307290680222]
  TongGuOCR is a layout-aware and token-augmented multimodal large language model (MLLM) for OCR of Chinese historical documents.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)
• ABot-OCR Technical Report [1.2258341760650364]
  ABot-OCR is an end-to-end vision-language model that transcribes a page image directly into clean markdown in a single forward pass.<n>We develop a dedicated data engine to provide large-scale, structurally consistent supervision.<n>ABot-OCR achieves state-of-the-art scores of 92.81 and 93.30 among all end-to-end systems.
  arXiv  Detail & Related papers  (2026-05-27T05:16:21Z)
• PP-OCRv5: A Specialized 5M-Parameter Model Rivaling Billion-Parameter Vision-Language Models on OCR Tasks [21.41974664575541]
  "OCR 2.0" and large-scale vision-language models (VLMs) have set new benchmarks in text recognition.<n>This paper introduces PP-OCRv5, a meticulously optimized, lightweight OCR system with merely 5 million parameters.
  arXiv  Detail & Related papers  (2026-03-25T14:54:40Z)
• Automated Invoice Data Extraction: Using LLM and OCR [0.0]
  This work introduces a holistic Artificial Intelligence (AI) platform combining OCR, deep learning, Large Language Models (LLMs) and graph analytics to achieve unprecedented extraction quality and consistency.
  arXiv  Detail & Related papers  (2025-11-01T19:05:09Z)
• Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency [31.095908827004695]
  Multimodal Large Language Models (MLLMs) have shown strong performance in document image tasks.<n>They struggle with Document Image Machine Translation (DIMT), which requires handling both cross-modal and cross-lingual challenges.<n>We introduce a novel fine-tuning paradigm, named Synchronously Self-Reviewing (SSR) its OCR proficiency, inspired by the concept "Bilingual Cognitive Advantage"
  arXiv  Detail & Related papers  (2025-07-11T05:02:06Z)
• PsOCR: Benchmarking Large Multimodal Models for Optical Character Recognition in Low-resource Pashto Language [2.1540520105079697]
  We develop a synthetic Pashto OCR dataset, PsOCR, consisting of one million images annotated with bounding boxes at word, line, and document levels.<n>PsOCR covers variations across 1,000 unique font families, colors, image sizes, and layouts.<n>A benchmark subset of 10K images was selected to evaluate the performance of several LMMs, including seven open-source models.<n> Gemini achieves the best performance among all models, whereas among open-source models, Qwen-7B stands out.
  arXiv  Detail & Related papers  (2025-05-15T07:58:38Z)
• OmniBench: Towards The Future of Universal Omni-Language Models [63.16606414452612]
  We introduce OmniBench, a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously.<n>Our evaluation reveals that open-source OLMs show significant limitations in instruction-following and reasoning in tri-modal contexts.<n>We advocate for developing more robust tri-modal integration techniques and training strategies to enhance OLM performance.
  arXiv  Detail & Related papers  (2024-09-23T17:59:05Z)
• SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition [55.2480439325792]
  Speech Meta In-Context LEarning (SMILE) is an innovative framework that combines meta-learning with speech in-context learning (SICL)<n>We show that SMILE consistently outperforms baseline methods in training-free few-shot multilingual ASR tasks.
  arXiv  Detail & Related papers  (2024-09-16T16:04:16Z)
• TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale [66.01943465390548]
  We introduce TriSum, a framework for distilling large language models' text summarization abilities into a compact, local model. Our method enhances local model performance on various benchmarks. It also improves interpretability by providing insights into the summarization rationale.
  arXiv  Detail & Related papers  (2024-03-15T14:36:38Z)
• Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
  We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
  arXiv  Detail & Related papers  (2024-02-16T13:53:26Z)
• GenKIE: Robust Generative Multimodal Document Key Information Extraction [24.365711528919313]
  Key information extraction from scanned documents has gained increasing attention because of its applications in various domains. We propose a novel generative end-to-end model, named GenKIE, to address the KIE task. One notable advantage of the generative model is that it enables automatic correction of OCR errors.
  arXiv  Detail & Related papers  (2023-10-24T19:12:56Z)
• EfficientOCR: An Extensible, Open-Source Package for Efficiently Digitizing World Knowledge [1.8434042562191815]
  EffOCR is a novel open-source optical character recognition (OCR) package. It meets both the computational and sample efficiency requirements for liberating texts at scale. EffOCR is cheap and sample efficient to train, as the model only needs to learn characters' visual appearance and not how they are used in sequence to form language.
  arXiv  Detail & Related papers  (2023-10-16T04:20:16Z)
• Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR Models [2.4654745083407175]
  We propose a new multi-rounds adaptation process that uses uncertainty to automate the annotation process.<n>This novel method streamlines data annotation and strategically selects data samples contributing most to model uncertainty.<n>Our results show that our approach leads to a 27% WER relative average improvement while requiring on average 45% less data than established baselines.
  arXiv  Detail & Related papers  (2023-06-03T13:11:37Z)
• Extrapolating Multilingual Understanding Models as Multilingual Generators [82.1355802012414]
  This paper explores methods to empower multilingual understanding models the generation abilities to get a unified model. We propose a textbfSemantic-textbfGuided textbfAlignment-then-Denoising (SGA) approach to adapt an encoder to a multilingual generator with a small number of new parameters.
  arXiv  Detail & Related papers  (2023-05-22T15:33:21Z)
• 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)
• Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition [71.96870151495536]
  We propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR) The AR procedure aims to modify the acoustic signals (from the target domain) to repurpose a pretrained SCR model. We evaluate the proposed AR-SCR system on three low-resource SCR datasets, including Arabic, Lithuanian, and dysarthric Mandarin speech.
  arXiv  Detail & Related papers  (2021-10-08T05:07:35Z)

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.