
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 de-
compositions 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. 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

Enhancing Handwritten Text Recognition with N-gram Sequence Decomposition and Multitask Learning

PS T4.1: Document Analysis and Retrieval

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Vasiliki Tassopoulou

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Enhancing Handwritten Text Recognition with N-gram Sequence Decomposition and Multitask Learning

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