Cross-Temporal Sinhala OCR: Page-Level Adaptation and Diachronic Analysis

• URL: http://arxiv.org/abs/2606.29378v1
• Date: Sun, 28 Jun 2026 13:01:54 GMT
• Title: Cross-Temporal Sinhala OCR: Page-Level Adaptation and Diachronic Analysis
• Abstract summary: There are no publicly available real-world datasets for page-level Sinhala OCR.<n>We introduce sinhala-ocr-lk-acts-1010, an annotated dataset of 1,010 page-level images and their transcriptions.<n>Three models based on deep learning-based visual language processing are fine-tuned using QLoRA.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sinhala is a morphologically rich abugida spoken by roughly 16 million people in Sri Lanka, and to date, there are no publicly available real-world datasets for page-level Sinhala OCR. All previous studies for assessing Sinhala OCR models have used artificially generated data. To bridge the gap, we introduce sinhala-ocr-lk-acts-1010, an annotated dataset of 1,010 page-level images and their transcriptions collected from Sri Lankan Legislative Acts published between 1981-1989 and 2000-2019, split into 707 training examples, 101 validation examples, and 202 testing examples. Three models based on deep learning-based visual language processing, namely DeepSeek-OCR V1, DeepSeek-OCR V2, and LightOnOCR-2-1B, are fine-tuned using QLoRA in 8 experiments conducted on consumer and cloud GPUs. LightOnOCR-2-1B is the top performer, achieving a CER of 1.05% across all test examples, outperforming state-of-the-art open-source OCR models such as Surya-OCR (8.84%) and Tesseract v5 (10.69%), as well as commercially available OCR models such as Google Document AI (2.06%). Our results suggest that LightOnOCR-2-1B outperforms other baselines on real-world OCR tasks and maintains consistent performance across all print periods, even when documents are severely degraded.

Related papers

• How Far Can Synthetic Data Take Thai OCR? [3.8641218447429657]
  We build a Thai OCR model adapted without OCR labels from real Thai document pages.<n>Synthetic data provide exact labels at scale, but "realism" conflates source domain, page context, typography, spatial structure, and glyph variation.<n>We evaluate each variant under page- and crop-level training on printed and handwritten Thai documents.
  arXiv  Detail & Related papers  (2026-09-03T09:46:19Z)
• TongGuOCR: A Layout-Aware and Token-Augmented OCR MLLM for Chinese Historical Documents [50.64307290680222]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)
• GlotOCR Bench: OCR Models Still Struggle Beyond a Handful of Unicode Scripts [58.92151016423978]
  We introduce GlotOCR Bench, a comprehensive benchmark evaluating OCR across 100+ Unicode scripts.<n>Our benchmark comprises clean and degraded image variants rendered from real multilingual texts.<n>We evaluate a broad suite of open-weight and proprietary vision-language models and find that most perform well on fewer than ten scripts.
  arXiv  Detail & Related papers  (2026-04-14T17:12:41Z)
• A Benchmark of State-Space Models vs. Transformers and BiLSTM-based Models for Historical Newspaper OCR [4.247423625723055]
  Linear-time State-Space Models (SSMs) are a scalable alternative to Transformer-based sequence modeling for OCR.<n>We present the first OCR architecture based on SSMs, combining a CNN visual encoder with bi-directional and autoregressive Mamba sequence modeling.<n>We show that Mamba-based models maintain competitive accuracy while halving inference time and exhibiting superior memory scaling.
  arXiv  Detail & Related papers  (2026-04-01T10:33:33Z)
• LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR [0.29410438275861583]
  We present textbfLightOnOCR-2-1B, a multilingual vision--language model that converts document images into clean, naturally ordered text without brittle OCR pipelines.<n>Trained on a large-scale, high-quality distillation mix with strong coverage of scans, French documents, and scientific PDFs, LightOnOCR-2 achieves state-of-the-art results on OlmOCR-Bench.<n>We release model checkpoints under Apache 2.0, and publicly release the dataset and textbfLightOnOCR-bbox-bench evaluation under their respective licenses.
  arXiv  Detail & Related papers  (2026-01-20T18:58:32Z)
• Zero-shot OCR Accuracy of Low-Resourced Languages: A Comparative Analysis on Sinhala and Tamil [1.256381443503838]
  This study presents a comparative analysis of the zero-shot performance of six distinct OCR engines on two Low-Resourced Languages (LRLs)<n>The selected engines include both commercial and open-source systems, aiming to evaluate the strengths of each category.<n>The Cloud Vision API, Surya, Document AI, and Tesseract were evaluated for both Sinhala and Tamil, while Subasa OCR and EasyOCR were examined for only one language due to their limitations.
  arXiv  Detail & Related papers  (2025-07-24T10:08:43Z)
• QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation [51.393569044134445]
  Large language models (LLMs) trained via reinforcement learning with verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit, automatable verification.<n> Extending RLVR to automatically generating hardware description languages (HDLs) like Verilog from natural-language (NL) specifications, however, poses three key challenges.<n>We introduce CodeV-R1, an RLVR framework for training Verilog generation LLMs.
  arXiv  Detail & Related papers  (2025-05-30T03:51:06Z)
• 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)
• NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study [57.45921692668671]
  We build the first benchmark dataset for short-form Image Super-resolution in the wild, termed KwaiSR.<n>This dataset is collected from the Kwai Platform, which is composed of two parts, i.e., synthetic and wild parts.<n>Based on the KwaiSR dataset, we organize the NTIRE 2025 challenge on a second short-form Video quality assessment and enhancement.
  arXiv  Detail & Related papers  (2025-04-21T10:04:26Z)
• Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs [67.54302101989542]
  Legal case retrieval aims to provide similar cases as references for a given fact description. Existing works mainly focus on case-to-case retrieval using lengthy queries. Data scale is insufficient to satisfy the training requirements of existing data-hungry neural models.
  arXiv  Detail & Related papers  (2024-10-09T06:26:39Z)
• LOCR: Location-Guided Transformer for Optical Character Recognition [55.195165959662795]
  We propose LOCR, a model that integrates location guiding into the transformer architecture during autoregression. We train the model on a dataset comprising over 77M text-location pairs from 125K academic document pages, including bounding boxes for words, tables and mathematical symbols. It outperforms all existing methods in our test set constructed from arXiv, as measured by edit distance, BLEU, METEOR and F-measure.
  arXiv  Detail & Related papers  (2024-03-04T15:34:12Z)
• User-Centric Evaluation of OCR Systems for Kwak'wala [92.73847703011353]
  We show that utilizing OCR reduces the time spent in the manual transcription of culturally valuable documents by over 50%. Our results demonstrate the potential benefits that OCR tools can have on downstream language documentation and revitalization efforts.
  arXiv  Detail & Related papers  (2023-02-26T21:41:15Z)

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.