Spell Checker for Punjabi Language Using Deep Neural Network

Kaur, Gurjit; Kaur, Kamaldeep; Singh, Parminder

doi:10.1109/icaccs.2019.8728369

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…In these cases, the deep network is helpful. Kaur et al 38 used the tree data structure to store the dictionary. They used a recurrent neural network that incorporates Long-short term memory (LSTM) unit to reduce the vector distance between the error word and the word in the dictionary to correct errors.…”

Section: Deep-learning-based Methodsmentioning

confidence: 99%

Correcting spelling mistakes in Persian texts with rules and deep learning methods

Kasmaiee,

Homayounpour

2023

Sci Rep

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This study aims to develop a system for automatically correcting spelling errors in Persian texts using two approaches: one that relies on rules and a common spelling mistake list and another that uses a deep neural network. The list of 700 common misspellings was compiled, and a database of 55,000 common Persian words was used to identify spelling errors in the rule-based approach. 112 rules were implemented for spelling correction, each providing suggested words for misspelled words. 2500 sentences were used for evaluation, with the word with the shortest Levenshtein distance selected for evaluation. In the deep learning approach, a deep encoder-decoder network that utilized long short-term memory (LSTM) with a word embedding layer was used as the base network, with FastText chosen as the word embedding layer. The base network was enhanced by adding convolutional and capsule layers. A database of 1.2 million sentences was created, with 800,000 for training, 200,000 for testing, and 200,000 for evaluation. The results showed that the network's performance with capsule and convolutional layers was similar to that of the base network. The network performed well in evaluation, achieving accuracy, precision, recall, F-measure, and bilingual evaluation understudy (Bleu) scores of 87%, 70%, 89%, 78%, and 84%, respectively.

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Section: Deep-learning-based Methodsmentioning

confidence: 99%

Correcting spelling mistakes in Persian texts with rules and deep learning methods

Kasmaiee,

Homayounpour

2023

Sci Rep

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“…The algorithm proposed in [21] is a combination of n-gram characters with a neural network. Ngrams and recurrent neural network (LSTM) are used for spell checking of the Punjabi language [22] and for the spelling correction process in Turkish [14].…”

Section: Related Workmentioning

confidence: 99%

Polish Word Recognition Based on n-Gram Methods

Wojcicki,

Zientarski

2024

IEEE Access

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Word recognition of Slavic languages is not an easy task due to the complicated declension of words and a variety of diacritical signs. Polish is a representative of West Slavic languages, which are written in Latin characters. Automatic handwritten word recognition in Slavic languages is not easy, due to the poor recognition rate of letters with diacritical signs and lack of good handwritten text corpora for languages with declension. The main aim of the research is to investigate the possibility of correcting typos made in the final phase of recognizing Polish. The method developed is based on letter recognition by means of convolutional neural networks (CNNs) and text matching algorithms for resulting words. At the first stage, we use a designed convolutional neural network for character recognition. At the second stage, after combining letters into words we apply a post-processing error correction method, which improves the efficiency of recognition of the misspelled words. We checked the efficiency of word matching for a few measures of similarity of words, i.e: edit distance (Damerau-Levenshtein), string matching (Sorensen-Dice) and list of candidates. In addition, we examine how word length and the number of misplaced letters affect the behaviour of the algorithms used. The analysis is carried out for bigram and trigram methods. By combining different methods to assess the similarity of words, better selection of lists of proposed words has been achieved. The article proposes an innovative method for correcting post-processing errors in recognizing Polish words with the efficiency of correct word matching ranging from 76% to 99%, depending on the measure and word length used.

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“…The central SDN controller is responsible for installing flow-handling rules, and the FSEM enables dynamic path adjustments based on a global network view. The POX controller platform was used to implement this proposed method [45].…”

Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

Review of SDN-based load-balancing methods, issues, challenges, and roadmap

Chandra Saxena,

Sabharwal,

Bajaj

2023

Int. j. electr. comput. eng. syst. (Online)

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The development of the Internet and smart end systems, such as smartphones and portable laptops, along with the emergence of cloud computing, social networks, and the Internet of Things, has brought about new network requirements. To meet these requirements, a new architecture called software-defined network (SDN) has been introduced. However, traffic distribution in SDN has raised challenges, especially in terms of uneven load distribution impacting network performance. To address this issue, several SDN load balancing (LB) techniques have been developed to improve efficiency. This article provides an overview of SDN and its effect on load balancing, highlighting key elements and discussing various load-balancing schemes based on existing solutions and research challenges. Additionally, the article outlines performance metrics used to evaluate these algorithms and suggests possible future research directions.

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Spell Checker for Punjabi Language Using Deep Neural Network

Cited by 9 publications

References 13 publications

Correcting spelling mistakes in Persian texts with rules and deep learning methods

Correcting spelling mistakes in Persian texts with rules and deep learning methods

Polish Word Recognition Based on n-Gram Methods

Review of SDN-based load-balancing methods, issues, challenges, and roadmap

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