A Bangla Spell Checking Technique to Facilitate Error Correction in Text Entry Environment

Mittra, Tanni; Nowrin, Sadia; Islam, Linta; Roy, Deepak Chandra

doi:10.1109/icasert.2019.8934461

Cited by 5 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A system for checking the spelling of an English word transliterated to Bengali, based on Levenshtein distance and unigram is presented in [18]. Another solution for Bengali in the form of smart spell checking is shown in [20]. The algorithm proposed in [21] is a combination of n-gram characters with a neural network.…”

Section: Related Workmentioning

confidence: 99%

Polish Word Recognition Based on n-Gram Methods

Wojcicki,

Zientarski

2024

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Polish Word Recognition Based on n-Gram Methods

Wojcicki,

Zientarski

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…N-gram models were generated for each candidate word. To identify non-word errors, a comprehensive Bangla word dictionary of around 600,000 words was compiled from various online repositories, newspapers, social networking sites, and Bangla blogs (Mittra et al, 2019). The study primarily addresses wordlevel errors and may lack in encompassing the full spectrum of errors, including contextual and informal errors H.A.Z.…”

Section: Related Workmentioning

confidence: 99%

“…2 Among the set of Bangla letters, certain complex characters contribute to the challenge of writing that results in a divergence between written and spoken communication. Phonetically similar alphabets in Bangla share the same pronunciation or phonetic utterance that allows interchangeability and consequently leads to errors within words as shown in Figure 1 (Mittra et al, 2019). For instance, Figure 1 shows the interchange of letters having similar phonetic qualities that generate error words impacting the language's authenticity and coherence (Sifat et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

BaTEClaCor: A Novel Dataset for Bangla Text Error Classification and Correction

Oshin,

Hoque,

Fahim

et al. 2023

Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

View full text Add to dashboard Cite

In the context of the dynamic realm of Bangla communication, online users are often prone to bending the language or making errors due to various factors. We attempt to detect, categorize, and correct those errors by employing several machine learning and deep learning models. To contribute to the preservation and authenticity of the Bangla language, we introduce a meticulously categorized organic dataset encompassing 10,000 authentic Bangla comments from a commonly used social media platform. Through rigorous comparative analysis of distinct models, our study highlights BanglaBERT's superiority in errorcategory classification and underscores the effectiveness of BanglaT5 for text correction. BanglaBERT achieves accuracy of 79.1% and 74.1% for binary and multiclass error-category classification while the BanglaBERT is finetuned and tested with our proposed dataset. Moreover, BanglaT5 achieves the best Rouge-L score (0.8459) when BanglaT5 is fine-tuned and tested with our corrected ground truths. Beyond algorithmic exploration, this endeavor represents a significant stride in enhancing the quality of digital discourse in the Banglaspeaking community, fostering linguistic precision and coherence in online interactions. The dataset and code is available at https: //github.com/SyedT1/BaTEClaCor.

show abstract

“…In 2018, Sooraj et al [18] proposed a spell checker for the Malayalam language based on a neural network. Later in 2019, Mittra et al [19] developed a Bangla spell checking technique to facilitate error correction in the text entry environment. They used 50,000 ngram sentences to detect and correct the errors.…”

Section: B Spell Checkermentioning

confidence: 99%

“…In terms of the size of the lexicon, our proposed spell checker shows major advancement over the existing solutions. Note that [19] is based on a lexicon with the size of 50,000 words, where our proposed lexicon consists of over 1 million words. On the other hand, [15] and [17] did not mention about their lexicon sizes and collection procedures.…”

Section: Comparison With the Existing Solutionsmentioning

confidence: 99%

Development of Bangla Spell and Grammar Checkers: Resource Creation and Evaluation

2021

View full text Add to dashboard Cite

A spell and grammar checker is profoundly essential for diverse publications especially for Bangla language in particular as it is spoken by millions of native speakers around the world. Considering the lack of research efforts, we demonstrate the development of a comprehensive Bangla spell and grammar checker with necessary resources. At first, a full-fledged and generalised Bangla monolingual corpus comprising over 100 million words has been built by scraping reputed, diversified online sources and then an extensive Bangla lexicon consisting of over 1 million unique words has been extracted from that corpus. Based on these corpus and lexicon, we have developed a combined spell and grammar checker application that simultaneously detects distinct spelling and grammatical mistakes and provides appropriate suggestions for both as well. The spell checker uses the Double Metaphone algorithm and Edit distance based on the distributed lexicons and numerical suffix dataset to detect all types of Bangla spelling mistakes with an accuracy rate of 97.21% individually. The grammar checker detects errors based on language model probability i.e. combination of bigram and trigram, and generates suggestions based on the Cosine similarity measure with the accuracy rate of 94.29% individually. The datasets and codes used in this work are freely available at https://git.io/JzJ4w.

show abstract

A Bangla Spell Checking Technique to Facilitate Error Correction in Text Entry Environment

Cited by 5 publications

References 8 publications

Polish Word Recognition Based on n-Gram Methods

Polish Word Recognition Based on n-Gram Methods

BaTEClaCor: A Novel Dataset for Bangla Text Error Classification and Correction

Development of Bangla Spell and Grammar Checkers: Resource Creation and Evaluation

Contact Info

Product

Resources

About