Revisiting N-Gram Models: Their Impact in Modern Neural Networks for Handwritten Text Recognition

arXiv:2404.19317v1 Announce Type: new
Abstract: In recent advances in automatic text recognition (ATR), deep neural networks have demonstrated the ability to implicitly capture language statistics, potentially reducing the need for traditional language models. This study directly addresses whether explicit language models, specifically n-gram models, still contribute to the performance of state-of-the-art deep learning architectures in the field of handwriting recognition. We evaluate two prominent neural network architectures, PyLaia and DAN, with and without the integration of explicit n-gram language models. …

abstract advances arxiv cs.cl cs.cv impact language language models modern networks neural networks recognition statistics study text type