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Pretraining boosts Devanagari OCR efficiency, slashing transcription needs

A new study published on arXiv investigates the efficiency of annotation for handwritten Devanagari recognition systems. Researchers found that supervised synthetic pretraining significantly reduces the number of transcribed words needed to achieve a useful recognizer. Specifically, a Character Error Rate (CER) of 0.50 was reached with only 81 transcribed words using this method, compared to 355 words required for random initialization, representing a 4.40x label multiplier. The study also explored the impact of transferring only the encoder and observed negative transfer effects from masked image modeling within certain budget ranges. AI

IMPACT Reduces the cost and time required to train effective handwritten text recognition systems, particularly for low-resource scripts.

RANK_REASON The cluster contains an academic paper detailing a study on machine learning methods for character recognition.

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Pretraining boosts Devanagari OCR efficiency, slashing transcription needs

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Manglesh Kumar Pandey, Sumit Kumar Banshal ·

    Measuring Annotation Efficiency for Handwritten Devanagari Recognition: Sample-Complexity Curves for Four Pretraining Regimes

    arXiv:2609.16859v1 Announce Type: cross Abstract: To train handwritten text recognition systems we need word images and their corresponding transcriptions, and these transcriptions are produced manually. For a script that can be read by only a small number of specialists, this ma…

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

    Measuring Annotation Efficiency for Handwritten Devanagari Recognition: Sample-Complexity Curves for Four Pretraining Regimes

    To train handwritten text recognition systems we need word images and their corresponding transcriptions, and these transcriptions are produced manually. For a script that can be read by only a small number of specialists, this manual transcription is a limitation, because the tr…