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- W4287547876 abstract "Current state-of-the-art approaches in the field of Handwritten Text Recognition are predominately single task with unigram, character level target units. In our work, we utilize a Multi-task Learning scheme, training the model to perform decompositions of the target sequence with target units of different granularity, from fine to coarse. We consider this method as a way to utilize n-gram information, implicitly, in the training process, while the final recognition is performed using only the unigram output. % in order to highlight the difference of the internal Unigram decoding of such a multi-task approach highlights the capability of the learned internal representations, imposed by the different n-grams at the training step. We select n-grams as our target units and we experiment from unigrams to fourgrams, namely subword level granularities. These multiple decompositions are learned from the network with task-specific CTC losses. Concerning network architectures, we propose two alternatives, namely the Hierarchical and the Block Multi-task. Overall, our proposed model, even though evaluated only on the unigram task, outperforms its counterpart single-task by absolute 2.52% WER and 1.02% CER, in the greedy decoding, without any computational overhead during inference, hinting towards successfully imposing an implicit language model." @default.
- W4287547876 created "2022-07-25" @default.
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- W4287547876 date "2020-12-28" @default.
- W4287547876 modified "2023-10-17" @default.
- W4287547876 title "Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning" @default.
- W4287547876 doi "https://doi.org/10.48550/arxiv.2012.14459" @default.
- W4287547876 hasPublicationYear "2020" @default.
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