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Words Ending Cer 5 Letters

Feature papers represent the most advanced research in the field with significant potential for high impact. Feature papers are submitted by scientific editors by special invitation or proposal and are peer-reviewed before publication.

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A feature paper or original research article can be a comprehensive review paper with concise and precise updates on the latest progress in a field, often involving a number of methods or approaches, or a comprehensive review paper that systematically examines the most exciting developments in science. literature. This type of paper provides an outlook on future research directions or possible applications.

Editor’s Choice articles are based on recommendations from scientific editors of journals from around the world. The editors believe that a small number of recently published articles in the journal will be of more interest or relevance to the authors. The goal is to provide a snapshot of the most exciting work published in the journal’s various research areas.

Arthur Flor de Sousa Neto 1, Byron Leite Dantas Bezerra 1, and Alejandro Hector Toselli 2

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

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The increasing presence of physical manuscripts in the digital environment makes it common for systems to offer automated mechanisms for offline handwriting text recognition (HTR). However, multiple script and spelling variations cause problems with recognition accuracy, and to reduce this problem, optical models can be used with language models to help encode text. Thus, in order to improve the results, dictionaries of characters and words are created from the database and linguistic constraints are created in the recognition process. Thus, this paper proposes the use of orthographic correction methods for post-text processing to achieve better results and remove the linguistic dependency between the optical model and the decoding stage. In addition, the architecture of the encoder-decoder neural network is developed and presented along with the training methodology to achieve the goal of spelling correction. To demonstrate the effectiveness of this new approach, we tested eight orthographic correction methods between state-of-the-art optical models for text recognition and standard statistics on five text strings widely known in the HTR system. and current approaches to neural networks in natural language processing (NLP). Finally, our proposed spelling correction model is statistically analyzed using HTR system benchmarks and achieves an average correction of 54% better than the state-of-the-art modernization method on the tested databases.

Deep learning; automatic handwriting text recognition; natural language processing; encoder-decoder model; spelling correction in-depth study; automatic handwriting text recognition; natural language processing; encoder-decoder model; spelling correction

Communication is an important communication and documentation tool around the world. In today’s digital age, it is common for physical manuscripts to be integrated into a technological environment where machines can understand the text of scanned images through a handwriting recognition process and display 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 efforts to digitize and transfer content using optical character recognition (OCR) technologies. .

There are two categories of OCR systems in this field: (i) online data, where input data is captured in real time through sensors; and (ii) in an offline form that derives information from static scenarios as in Figs. However, there is recognition of published text and manuscripts in the autonomous category [8]. Unlike the printed text scenario, automatic handwritten text recognition (HTR) is more challenging to achieve because there are many different variations of the same sentence for the same writer [8]. Fortunately, HTR systems have advanced significantly since using Hidden Markov Model (HMM) for text recognition [2, 9, 10, 11]. Today, with the use of deep neural networks (Deep Learning), the recognition process can be performed more reliably at different levels of text segmentation, namely [12], word [13, 14], line [15] and even paragraph [16] levels. However, they still do not achieve satisfactory results in scenarios with unlimited vocabulary [6], and to reduce this problem it is common to perform text encoding together with post-processing using Natural Language Processing (NLP) techniques [17]. , especially the statistical approach [18].

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In this regard, tools such as the Stanford Research Institute Language Model (SRILM) [19] and Kaldi [20] have gained traction in recent years by performing text encoding using a language model. In fact, these two tools are currently the most widely used in HTR systems, and optical modeling results are improved at this step [2, 15, 21]. In addition, it is necessary to create and use a dictionary of structural characteristics using statistical methods or based on external corporate databases [18]. Thus, text encoding in an HTR system is limited by this dictionary, which in turn has limited database connectivity, limiting its use in new text scenarios, especially in multilingual systems.

On the other hand, learning areas in NLP, such as Machine Translation [22, 23] and Grammatical Error Correction [24, 25], which work with text processing, classification and correction, have yielded promising results with neural systems approaches. recent years [26, 27, 28]. Encoder-decoder models have developed significantly in NLP, also known as sequencing [26, 29], for applications that require extensive linguistic knowledge, and many times statistical approaches have limitations in the linguistic context [29]. Furthermore, with better results, these models have been extended with the Attention mechanism and more recently introduced fully attention-based models [22, 32, 33].

Therefore, the main goal of this work is to use orthographic correction methods in HTR system processing (at line level and free segmentation level) to obtain competitive results to traditional decoding method and remove steps. recognition process. In other words, allowing the HTR system to be integrated with any processing approach, regardless of the vocabulary between the two systems.

In this paper, we use and analyze the use of methods that vary between statistical approaches [2, 18, 34] and differ from neural networks in recent language learning, focusing on orthographic corrections, such as consistency of the attentional mechanism. [30, 31] and Transformer [22] models. For better analysis in different data scenarios, we used five databases in the experiment: Bentham [35]; Department of Informatics 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 Gall [38]; and Washington [39]. In addition, we use three optical models as HTR systems: Bluche [40]; Puigcerver [15]; and the proposed model. Therefore, we created different combinations between databases and application methods, creating different analysis tasks. An open source implementation (https://github.com/arthurflor23/handwritten-text-recognition, https://github.com/arthurflor23/spelling-correction) is implemented to increase output on request.

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The rest of this article is organized as follows: Section 2 details the operation of the automatic handwriting recognition system. Then, in Section 3, the orthographic correction process is presented using statistical and neural network approaches. Section 4 details the best HTR system produced in our analysis, the optical model, and the proposed encoder-decoder model for orthographic correction. Section 5 describes the methodology and experimental design. Section 6 presents the experimental results for each database and technique. In Section 7, the results are presented and discussed. Finally, Section 8 presents the conclusions that conclude the paper.

Offline handwritten text recognition (HTR) has grown over the past few decades for two reasons: (i) using previously developed training and recognition concepts and techniques in automatic speech recognition (ASR); and (ii) increasing the number of publicly available databases for training and testing. Thus, optical models in HTR systems are typically associated with language models at the character or word level to facilitate text recognition [2, 41].

The most common approaches to HTR are based on N-gram language models (statistical approach) and hidden Markov Model (HMM) optical modeling with Gaussian mixture emission distribution [42], which has been improved recently with emission probabilities by multilayer sensors [21]. However, significant improvements in HTR recognition accuracy have been achieved using artificial neural networks as optical models