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# Easter2
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Easter2.0: IMPROVING CONVOLUTIONAL MODELS FOR HANDWRITTEN TEXT RECOGNITION
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# Easter2.0: IMPROVING CONVOLUTIONAL MODELS FOR HANDWRITTEN TEXT RECOGNITION
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This repo provides the model and code for our paper: To Be Updated.
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[[PDF]](To Be Updated.)
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### Overview
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In this paper, we proposed a convolutional architecture for the task of handwritten text recognition that utilizes only 1D
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convolutions, dense residual connections and a SE module. We also proposed a simple and effective data augmentation
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technique-T ACo useful for OCR/HTR tasks. We have presented experimental study on components of Easter2.0
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architecture including dense residual connections, normalization choices, SE module, TACo variations and few-shot
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training. Our work achieves SOTA results on IAM-Test set when training data is limited, also Easter2.0 has very
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small number of trainable parameters compared to other solutions. The proposed architecture can be used in search of
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smaller, faster and efficient OCR/HTR solutions when available annotated data is limited.
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## Citation
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If you find our work helpful, please cite the following:
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To be updated.

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