## 1 Introduction  
Writing is an essential communication and documentation tool worldwide. Currently, in the digital age, the integration of physical manuscripts in the technological environment is becoming common, in which machines can understand the text of scanned images through the process of handwriting recognition and represent them in the digital context for later use [1]. Historical manuscripts [2], medical prescriptions [3], documents [4], and general forms [5] are some scenarios that require manual effort to digitize and transcribe content into the digital environment through Optical Character Recognition (OCR) technologies [6]. In this field, OCR systems have two categories: (i) online, in which the input information is obtained in real time through sensors; and (ii) offline, which obtains data from static scenarios, as in the case of images [7]. Still, in the offline category, there is recognition of printed text and manuscript [8]. Unlike the printed text scenario, offline Handwritten Text Recognition (HTR) is more complex to achieve its goal, as for the same writer there are numerous variations in a single sentence [8]. Fortunately, HTR systems have evolved considerably since the use of the Hidden Markov Model (HMM) for text recognition [2; 9; 10; 11]. Currently, with the use of Deep Neural Networks (Deep Learning), it is possible to more assertively perform the recognition process at different levels of text segmentation that is, character [12], word [13; 14], line [15] and even the paragraph [16] levels. However, in scenarios with an unrestricted dictionary, they still do not achieve satisfactory results [6] and, to minimize this problem, it is common to perform text decoding in conjunction with post-processing using Natural Language Processing (NLP) techniques [17], specifically the statistical approach [18]. In this context, tools such as Stanford Research Institute Language Model (SRILM) [19] and Kaldi [20] have gained space in the last years, by performing text decoding through a language model. In fact, these two tools have become the most commonly used currently in HTR systems and the results of optical model are improved in this step [2; 15; 21]. In addition, through statistical methods, it is essential to create and use a structured character dictionary based on the dataset used or external corpora [18]. Thus, text decoding in an HTR system becomes restricted to this dictionary, which, in turn, has correlation to the dataset, causing a limitation in its application in new text scenarios, especially in multi-language systems. On the other hand, fields of study in NLP, such as Machine Translation [22; 23] and Grammatical Error Correction [24; 25], which work with text processing, classification, and correction, have brought promising results with neural networks approaches in recent years [26; 27; 28]. The application of encoder-decoder models, also known as Sequence to sequence [26; 29], has grown considerably in the field of NLP for applications that require huge linguistic knowledge and, many times, statistical approaches have limitations of linguistic context [29]. In addition, these models were extended with the Attention mechanism [30; 31], achieving even better results, and, more recently, presented models based entirely on Attention [22; 32; 33]. Therefore, the main goal of this work is to apply alternative spelling correction techniques in the post-processing of an HTR system (at line level and free segmentation level), in order to obtain competitive results to the traditional method of decoding and disconnect the stages of the recognition process. In other words, to enable the HTR system to integrate with any post-processing approach, regardless of dictionary between both systems. In this paper, we apply and analyze applications of techniques with a focus on spelling correction, varying between statistical approaches [2; 18; 34] and the most recent with neural networks in the field of linguistics, such as Sequence to sequence with Attention mechanism [30; 31] and Transformer [22] models.