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New Sinhala OCR Dataset and LightOnOCR-2-1B Achieve State-of-the-Art Performance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new dataset, sinhala-ocr-lk-acts-1010, to improve Optical Character Recognition (OCR) for Sinhala, a language spoken by approximately 16 million people. This dataset comprises 1,010 page-level images and transcriptions from Sri Lankan Legislative Acts spanning two decades. Fine-tuning models like DeepSeek-OCR V1, DeepSeek-OCR V2, and LightOnOCR-2-1B using QLoRA, the study found LightOnOCR-2-1B to be the top performer. It achieved a Character Error Rate (CER) of 1.05%, significantly outperforming other open-source and commercial OCR models, including Surya-OCR, Tesseract v5, and Google Document AI. AI

IMPACT Advances OCR capabilities for low-resource languages, potentially enabling new applications for Sinhala text processing.

RANK_REASON The item describes a new dataset and fine-tuned models for Sinhala OCR, including benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Sinhala OCR Dataset and LightOnOCR-2-1B Achieve State-of-the-Art Performance

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The item describes a new dataset and fine-tuned models for Sinhala OCR, including benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    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…

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