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Author: Arthur Flor de Sousa Neto 1, Byron Leite Dantas Bezerra 1, * and Alejandro Héctor Toselli 2

Received: September 16, 2020 / Revised: October 26, 2020 / Accepted: October 27, 2020 / Published: October 31, 2020

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The increasing portability of physical manuscripts to the digital environment makes it common for systems to provide automatic offline Handwritten Text Recognition (HTR) mechanisms. However, various scenarios and writing variations present challenges in recognition accuracy, and to minimize this problem, optical models can be used to help decode text with language models. Thus, in order to improve the results, dictionaries of characters and words are created from the data set and language constraints are created in the recognition process. In this way, this work proposes the use of spelling correction techniques for the post-processing of the text, in order to obtain better results and to eliminate the linguistic dependency between the optical model and the decoding phase. In addition, an encoder-decoder neural network architecture is developed and presented along with a training methodology to achieve the goal of spelling correction. To demonstrate the effectiveness of this new approach, we conducted an experiment on five datasets of text lines, well-known in the field of HTR, three state-of-the-art Optical Models for text recognition, and eight spelling correction techniques, among traditional statistics. and current approaches to neural networks in the field of Natural Language Processing (NLP). Finally, the proposed spelling correction model is statistically analyzed through measurements of the HTR system, reaching an average sentence correction of 54% better than the state-of-the-art decoding method on the tested datasets.

Deep learning; offline handwritten text recognition; natural language processing; encoder-decoder model; deep learning spelling correction; offline handwritten text recognition; natural language processing; encoder-decoder model; spelling correction

Writing is an essential communication and documentation tool all over the world. Today, in the digital age, it is becoming common to integrate physical manuscripts into a technological environment, where machines can understand the text of scanned images through the process of handwriting recognition and represent them in a digital context for later use [1]. Historical manuscripts [2], medical prescriptions [3], documents [4] and general forms [5] are some of the scenarios that require manual effort to digitize and transcribe content into the digital environment using optical character recognition (OCR) technologies [6] .

In this field, OCR systems have two categories: (i) online, where 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]. However, in the offline category, printed text and manuscript are recognized [8]. Unlike the printed text scenario, offline Handwritten Text Recognition (HTR) is more complex to achieve its goal, as there are many variations for the same writer in a single sentence [8]. Fortunately, HTR systems have evolved a lot since the Hidden Markov Model (HMM) was used for text recognition [2, 9, 10, 11]. Today, using Deep Neural Networks (Deep Learning), it is possible to make the recognition process more assertive at different levels of text segmentation, i.e. character [12], word [13, 14], line [15] and paragraph [16]. levels too. However, in scenarios with an unlimited vocabulary, they still do not obtain good results [6] and, to minimize this problem, it is common to perform text decoding together with post-processing, using Natural Language Processing (NLP) techniques [17] , specifically, a statistical approach [ 18].

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In this context, tools such as the Stanford Research Institute Language Model (SRILM) [19] and Kaldi [20] have gained space in recent years, performing text decoding through a language model. In fact, these two tools have become the most used in HTR systems today and the results of the optical model are improved in this step [2, 15, 21]. Furthermore, through statistical methods, it is essential to create and use a structured dictionary of characters based on the dataset used or external corpora [18]. Thus, text decoding in an HTR system is limited to this dictionary, which in turn correlates with the data set, leading to a limitation in its application in new text scenarios, especially in multilingual systems.

On the other hand, NLP learning fields such as Automatic Translation [22, 23] and Grammatical Error Correction [24, 25], which work with text processing, classification and correction, have brought promising results with neural network approaches. recent years [26, 27, 28]. The application of encoder-decoder models, also known as Sequence to sequence [26, 29], has grown significantly in the field of NLP for applications that require a lot of linguistic knowledge and, often, statistical approaches have limitations of the linguistic context [29] . Moreover, these models were extended with the Attention mechanism [30, 31], obtaining even better results, and recently, models based on the whole Attention were presented [22, 32, 33].

Therefore, the main objective of this work is to apply alternative spelling correction techniques (at the line level and at the free segmentation level) in the post-processing of an HTR system, to achieve competitive results to the traditional method of decoding and decoupling stages. of the recognition process. In other words, enabling the HTR system to be integrated with any post-processing approach, regardless of the vocabulary between the two systems.

In this article, we apply and analyze the applications of techniques focused on orthographic correction, among statistical approaches [2, 18, 34] and the most recent with neural networks in the field of linguistics, such as the Sequence to sequence with Attention mechanism. [30, 31] and Transformer [22] models. To better analyze different data scenarios, we used five data sets in the experiment: Bentham [35]; Department of Computer Science and Applied Mathematics (Institut für Informatik und Angewandte Mathematik, IAM) [36]; Recognition and indexing of handwritten documents and facsimiles (Reconnaissance et Indexation de données Manuscrites et de fac similÉS, RIMES) [37]; St. Gall [38]; and Washington [39]. In addition, we also use three optical models as HTR systems: Bluche [40]; Puigcerver [15]; and the proposed model. In this way, we created many combinations of datasets and applied techniques, creating various analysis workflows. An open source (https://github.com/arthurflor23/handwritten-text-recognition, https://github.com/arthurflor23/spelling-correction) implementation for reproducibility of results is also available upon request.

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The remainder of this paper is organized as follows: Section 2 describes the process of the offline Handwritten Text Recognition system. Then, Section 3 presents the spelling correction process using statistical networks and neural networks approaches. Section 4 presents the best overall HTR system generated in our analysis, specifying the optical model and the proposed encoder-decoder model for spelling correction. Section 5 describes the methodology and experimental setup. Section 6 presents the experimental results for each data set and technique. In Section 7, the results are interpreted and discussed. Finally, section 8 presents the conclusions that summarize the article.

Offline handwritten text recognition (HTR) has evolved over the past decades for two reasons: (i) the use of previously developed training and recognition concepts and techniques in the field of automatic speech recognition (ASR); and (ii) a growing set of publicly available data for training and testing. In this way, optical patterns in HTR systems are linked to language patterns, usually at the character or word level, to make text recognition plausible [2, 41].

The most traditional approaches to HTR are based on N-gram language models (statistical approach) and Hidden Markov Model (HMM) optical modeling with Gaussian mixture emission distributions [42], recently improved by multilayer perceptrons with emission probabilities [21]. However, significant improvements in HTR detection accuracy have been achieved using artificial neural networks as optical models, specifically.