  Optical Character Recognition is a technique that converts document images
into searchable and editable text, making it a valuable tool for processing
scanned documents. While the Farsi language stands as a prominent and official
language in Asia, efforts to develop efficient methods for recognizing Farsi
printed text have been relatively limited. This is primarily attributed to the
languages distinctive features, such as cursive form, the resemblance between
certain alphabet characters, and the presence of numerous diacritics and dot
placement. On the other hand, given the substantial training sample
requirements of deep-based architectures for effective performance, the
development of such datasets holds paramount significance. In light of these
concerns, this paper aims to present a novel large-scale dataset, IDPL-PFOD2,
tailored for Farsi printed text recognition. The dataset comprises 2003541
images featuring a wide variety of fonts, styles, and sizes. This dataset is an
extension of the previously introduced IDPL-PFOD dataset, offering a
substantial increase in both volume and diversity. Furthermore, the datasets
effectiveness is assessed through the utilization of both CRNN-based and Vision
Transformer architectures. The CRNN-based model achieves a baseline accuracy
rate of 78.49% and a normalized edit distance of 97.72%, while the Vision
Transformer architecture attains an accuracy of 81.32% and a normalized edit
distance of 98.74%.
