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Mamba-based OCR models show speed gains but lag on handwriting

By PulseAugur Editorial · [1 sources] ·

A new study explores the performance of Mamba-based State-Space Models (SSMs) for Optical Character Recognition (OCR) tasks, particularly focusing on their scalability from short lines to full paragraphs. Researchers found that while SSMs offer significant speed advantages over Transformers for long sequences, achieving comparable accuracy on clean synthetic data, they struggle with real-world handwriting due to data scarcity. The study identifies decoder depth and state dimension as key hyperparameters for improving long-sequence accuracy in SSMs for OCR. AI

IMPACT SSMs show promise for faster OCR on long texts but require more data for handwriting recognition.

RANK_REASON Research paper detailing an ablation study of a specific model architecture for a particular task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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

Mamba-based OCR models show speed gains but lag on handwriting

COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Thierry Paquet ·

    Scaling State-Space Models from Lines to Paragraphs: An Ablation of Mamba-based OCR

    End-to-end OCR increasingly relies on autoregressive sequence models, where the quadratic cost of Transformer attention limits efficient transcription of long, paragraph-level text. State-Space Models (SSMs) such as Mamba offer linear-time decoding and have recently been shown to…

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