A Self-attention Based Model for Offline Handwritten Text Recognition

Ly, Nam Tuan; Ngo, Trung Tan; Nakagawa, Masaki

doi:10.1007/978-3-031-02444-3_27

Cited by 3 publications

(1 citation statement)

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“…The self-additive-attention technique [25] involves the evaluation of a context vector. This vector is then concatenated with the output of a BiLISTM and passed through a fully connected network and a softmax activation function as formulated in Eq.…”

Section: Self-additive-attentionmentioning

confidence: 99%

Enhanced Recognition of Offline Marathi Handwriting via a Self-Additive Attention Mechanism

Chikmurge,

Raghunathan

2023

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Background: Offline Marathi handwriting recognition presents a significant challenge due to the script's complexity and the variability of individual writing styles. Methods: In the present work, an advanced encoder-decoder framework is introduced, wherein a novel selfadditive-attention mechanism is integrated. This model capitalizes on a Convolutional Neural Network paired with a Joint Scale Feature Extractor (CNN-JSE) to discern low-level image features within the IIIT-HW-Dev dataset. Such features are subsequently fed into an encoder model that employs a dual-phase fusion process: initially leveraging a Bidirectional Long Short-Term Memory (BiLSTM) network, followed by the self-additive-attention mechanism to maintain dependencies over extensive sequences. Results: The fusion output, comprising BiLSTM and self-additive attention data, is conveyed to a Connectionist Temporal Classifier (CTC) decoder. This decoder adeptly identifies character sequences within Marathi words. The introduction of self-additive attention alongside BiLSTM is instrumental in preserving dependencies that are both long-range and multi-stage. Performance Evaluation: The efficacy of the proposed system was rigorously evaluated on the multi-author IIIT-HW-Dev dataset. Performance metrics, specifically Character Error Rate (CER) and Word Error Rate (WER), were employed for benchmarking against various established models. Conclusion: The proposed methodology demonstrates a significant enhancement in the recognition of handwritten Marathi text, thus facilitating the conversion of handwritten documents into machine-editable text. The utilization of selfadditive attention within the BiLSTM framework underscores its potential in capturing complex dependencies, setting a new precedent in automated handwriting recognition technologies. Significance: This study not only paves the way for increased accuracy in handwritten text recognition but also contributes a novel approach to the encoding of feature sequences, which may be applicable to a broad range of pattern recognition applications.

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Section: Self-additive-attentionmentioning

confidence: 99%