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. Our experiments on three
datasets - IAM, RIMES, and NorHand v2 - at both line and page level,
investigate optimal parameters for n-gram models, including their order,
weight, smoothing methods and tokenization level. The results show that
incorporating character or subword n-gram models significantly improves the
performance of ATR models on all datasets, challenging the notion that deep
learning models alone are sufficient for optimal performance. In particular,
the combination of DAN with a character language model outperforms current
benchmarks, confirming the value of hybrid approaches in modern document
analysis systems.

该研究探讨了在手写识别领域中，深度神经网络结构集成显式 n-gram 语言模型是否能提高性能，结果表明，结合字符或子词的 n-gram 模型能显著改善自动文本识别系统在多个数据集上的性能，挑战了仅仅使用深度学习模型就能够达到最优性能的观点，特别是 DAN 结合字符语言模型的组合超过了目前的基准，确认了混合方法在现代文档分析系统中的价值。

重温 N-Gram 模型：对手写文本识别现代神经网络的影响

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

PyLaia is one of the most popular open-source software for Automatic Text
Recognition (ATR), delivering strong performance in terms of speed and
accuracy. In this paper, we outline our recent contributions to the PyLaia
library, focusing on the incorporation of reliable confidence scores and the
integration of statistical language modeling during decoding. Our
implementation provides an easy way to combine PyLaia with n-grams language
models at different levels. One of the highlights of this work is that language
models are completely auto-tuned: they can be built and used easily without any
expert knowledge, and without requiring any additional data. To demonstrate the
significance of our contribution, we evaluate PyLaia's performance on twelve
datasets, both with and without language modelling. The results show that
decoding with small language models improves the Word Error Rate by 13% and the
Character Error Rate by 12% in average. Additionally, we conduct an analysis of
confidence scores and highlight the importance of calibration techniques. Our
implementation is publicly available in the official PyLaia repository at
this https URL, and twelve open-source models are
released on Hugging Face.

PyLaia 是最受欢迎的开源自动文本识别（ATR）软件之一，最近改进包括可靠的置信度评分和解码过程中统计语言建模的整合，结果显示使用小型语言模型进行解码平均会将词错误率提高 13% 和字符错误率提高 12%。