Text continues to remain a relevant form of representation for information.
Text documents are created either in digital native platforms or through the
conversion of other media files such as images and speech. While the digital
native text is invariably obtained through physical or virtual keyboards,
technologies such as OCR and speech recognition are utilized to transform the
images and speech signals into text content. All these variety of mechanisms of
text generation also introduce errors into the captured text.
This project aims at analyzing different kinds of error that occurs in text
documents. The work employs two of the advanced deep neural network-based
language models, namely, BART and MarianMT, to rectify the anomalies present in
the text. Transfer learning of these models with available dataset is performed
to finetune their capacity for error correction. A comparative study is
conducted to investigate the effectiveness of these models in handling each of
the defined error categories. It is observed that while both models can bring
down the erroneous sentences by 20+%, BART can handle spelling errors far
better (24.6%) than grammatical errors (8.8%).

这项研究旨在分析在文本文件中出现的各种错误，并利用两种先进的深度神经网络语言模型 (BART 和 MarianMT) 纠正文本中存在的异常。通过可用的数据集进行这些模型的迁移学习，以调整其错误纠正能力。通过比较研究发现，虽然这两种模型都可以将错误句子减少 20+%，但 BART 在拼写错误 (24.6%) 方面表现得更好，而在语法错误 (8.8%) 方面表现较差。