  Spelling correction is a remarkable challenge in the field of natural
language processing. The objective of spelling correction tasks is to recognize
and rectify spelling errors automatically. The development of applications that
can effectually diagnose and correct Persian spelling and grammatical errors
has become more important in order to improve the quality of Persian text. The
Typographical Error Type Detection in Persian is a relatively understudied
area. Therefore, this paper presents a compelling approach for detecting
typographical errors in Persian texts. Our work includes the presentation of a
publicly available dataset called FarsTypo, which comprises 3.4 million words
arranged in chronological order and tagged with their corresponding
part-of-speech. These words cover a wide range of topics and linguistic styles.
We develop an algorithm designed to apply Persian-specific errors to a scalable
portion of these words, resulting in a parallel dataset of correct and
incorrect words. By leveraging FarsTypo, we establish a strong foundation and
conduct a thorough comparison of various methodologies employing different
architectures. Additionally, we introduce a groundbreaking Deep Sequential
Neural Network that utilizes both word and character embeddings, along with
bidirectional LSTM layers, for token classification aimed at detecting
typographical errors across 51 distinct classes. Our approach is contrasted
with highly advanced industrial systems that, unlike this study, have been
developed using a diverse range of resources. The outcomes of our final method
proved to be highly competitive, achieving an accuracy of 97.62%, precision of
98.83%, recall of 98.61%, and surpassing others in terms of speed.
