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Words Ending In Cer 5 Letters

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

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

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The increasing portability of physical manuscripts to the digital environment makes it common for systems to offer automatic mechanisms for offline Handwritten Text Recognition (HTR). However, several scenarios and writing variations bring challenges in terms of recognition accuracy, and, to reduce this problem, optical models can be used with language models to aid in text decoding. Therefore, with the aim of improving results, dictionaries of characters and words are generated from the data set and linguistic constraints are created in the recognition process. In this way, this work proposes the use of spelling correction techniques for text post-processing to achieve better results and eliminate the linguistic dependency between the optical model and the decoding stage. In addition, an encoder-decoder neural network architecture in combination with a training methodology is developed and presented to achieve the goal of spelling correction. In order to demonstrate the effectiveness of this new method, we conducted an experiment on five datasets of text lines, which are widely 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 methods of neural networks in the field of Natural Language Processing (NLP). Finally, our proposed spelling correction model is statistically analyzed through HTR system metrics, reaching an average sentence correction of 54% higher than the state-of-the-art decoding method in 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; correct spelling

Writing is an essential communication and documentation tool around the world. Currently, in the digital age, the integration of physical manuscripts in the technological environment is becoming common, where 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 technologies (OCR) [6].

In this field, OCR systems have two categories: (i) online, where the input information is collected in real time through sensors; and (ii) offline, which obtains data from static scenarios, as in the case of images [7]. Yet, in the offline category, there is recognition of printed and manuscript text [8]. Unlike the printed text scenario, offline Handwritten Text Recognition (HTR) is more complex to achieve its goal, because for the same author there are numerous variations in one sentence [8]. Fortunately, HTR systems have evolved significantly 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 perform the recognition process more precisely at different levels of text segmentation, that is, character [12], word [13, 14 ], line [15] and even the paragraph levels [16]. However, in scenarios with an unlimited dictionary, they still do not achieve satisfactory results [6] and, to reduce this problem, it is common to perform text decoding in conjunction with post-processing using Natural Language Processing techniques ( NLP) [17] , specifically the statistical method [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, by performing text decoding through a language model. In fact, these two tools are currently the most frequently used in HTR systems and optical model results 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 data set used or an external corpora [18]. Therefore, text decoding in an HTR system is limited to this dictionary, which, in turn, is correlated with the data set, thus limiting 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 network methods in 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 huge linguistic knowledge and, many times, statistical methods have inherent limitations. -linguistic text [29] . In addition, these models were expanded 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 aim of this work is to apply alternative spelling correction techniques in the post-processing of the HTR system (at line level and free segmentation level), in order to obtain competitive results to the traditional method of decoding and decoupling the steps. of the recognition process. In other words, to enable the HTR system to integrate with any post-processing method, regardless of the dictionary between the two systems.

In this paper, we apply and analyze the 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 an Attention mechanism [30, 31] and Transformer [22] models. To obtain a better analysis in different data scenarios, five data sets were used 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]; Saint Can [38]; and Washington [39]. In addition, we also use three optical models as HTR system: Bluche [40]; Puigcerver [15]; and the proposed model. In this way, we created a wide variety of combinations between datasets and applied techniques, creating diverse analysis workflows. An open source implementation (https://github.com/arthurflor23/handwritten-text-recognition, https://github.com/arthurflor23/spelling-correction) for reproducing results is also provided on request.

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The rest of this article is organized as follows: in Section 2, the process of the offline Handwritten Text Recognition system is detailed. Then, in Section 3, the spelling correction process is presented through statistical and neural network methods. In Section 4, the overall best HTR system produced in our analysis is presented, detailing the optical model and the proposed encoder-decoder model for spelling correction. In Section 5, the methodology and experimental setup are explained. In Section 6, the results obtained from the experiment in each data set and technique are presented. 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 in the last few decades for two reasons: (i) the use of training and recognition concepts and techniques previously developed in the field of Automatic Speech Recognition (ASR); and (ii) the increasing number of publicly available datasets for training and testing. In this way, the optical models in HTR systems are generally linked to language models, usually at the character or word level, to make the text recognition plausible [2, 41].

The most traditional methods of HTR are based on N-gram language models (statistical method) and Hidden Markov Model (HMM) optical modeling with gaussian mixture emission distributions [42], most recently improved with emission probabilities by multilayer perceptrons [21]. However, notable improvements in HTR recognition accuracy have been achieved through artificial neural networks such as optical models, specifically