Digital technologies have led to an influx of text created daily in a variety
of languages, styles, and formats. A great deal of the popularity of
spell-checking systems can be attributed to this phenomenon since they are
crucial to polishing the digitally conceived text. In this study, we tackle
Typographical Error Type Detection in Persian, which has been relatively
understudied. In this paper, we present a public dataset named FarsTypo,
containing 3.4 million chronologically ordered and part-of-speech tagged words
of diverse topics and linguistic styles. An algorithm for applying
Persian-specific errors is developed and applied to a scalable size of these
words, forming a parallel dataset of correct and incorrect words. Using
FarsTypo, we establish a firm baseline and compare different methodologies
using various architectures. In addition, we present a novel Many-to-Many Deep
Sequential Neural Network to perform token classification using both word and
character embeddings in combination with bidirectional LSTM layers to detect
typographical errors across 51 classes. We compare our approach with
highly-advanced industrial systems that, unlike this study, have been developed
utilizing a variety of resources. The results of our final method were
competitive in that we achieved an accuracy of 97.62%, a precision of 98.83%, a
recall of 98.61%, and outperformed the rest in terms of speed.

本研究针对波斯语（Persian）的排版错误类型检测展开研究，构建了一套公开数据集 FarsTypo，使用神经网络结合 Word 和 Character Embeddings 进行分类，获得了 97.62% 的准确率，98.83% 的精确度，并在速度方面表现优异。