BNNet: Towards A Comprehensive Bangla WordNet

Islam, Saimum; Lotif, Abdul; Akther, Aysha; Alam, Kazi Masudul

doi:10.1109/iccit51783.2020.9392650

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…They have combined freely available online materials to build comprehensive Bangla word resources. It has 247748 unique Bangla words [29]. The dataset is useful for word recognition, writer identification, handwritten word segmentation, and word generation.…”

Section: Bangla Word Dictionarymentioning

confidence: 99%

Handwritten Word Recognition using Deep Learning Approach: A Novel Way of Generating Handwritten Words

Akter

Shahriar

Cuzzocrea

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

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A handwritten word recognition system comes with issues such as-lack of large and diverse datasets. It is necessary to resolve such issues since millions of official documents can be digitized by training deep learning models using a large and diverse dataset. Due to the lack of data availability, the trained model does not give the expected result. Thus, it has a high chance of showing poor results. This paper proposes a novel way of generating diverse handwritten word images using handwritten characters. The idea of our project is to train the BiLSTM-CTC architecture with generated synthetic handwritten words. The whole approach shows the process of generating two types of large and diverse handwritten word datasets: overlapped and non-overlapped. Since handwritten words also have issues like overlapping between two characters, we have tried to put it into our experimental part. We have also demonstrated the process of recognizing handwritten documents using the deep learning model. For the experiments, we have targeted the Bangla language, which lacks the handwritten word dataset, and can be followed for any language. Our approach is less complex and less costly than traditional GAN models. Finally, we have evaluated our model using Word Error Rate (WER), accuracy, f1-score, precision, and recall metrics. The model gives 39% WER score, 92% percent accuracy, and 92% percent f1 scores using nonoverlapped data and 63% percent WER score, 83% percent accuracy, and 85% percent f1 scores using overlapped data.

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Section: Bangla Word Dictionarymentioning

confidence: 99%

Handwritten Word Recognition using Deep Learning Approach: A Novel Way of Generating Handwritten Words

Akter

Shahriar

Cuzzocrea

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

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“…In recent years, a few Bangla language researchers have demonstrated a keen interest in Bangla corpus development and Bangla text analysis. Some notable progress has been documented in corpus creation in [15], [16], [17], [18], [19], [20] and knowledge engineering on Bangla language in [21], [22], [23], [24], etc. The first electronic Bangla corpus was constructed by the Central Institute of Indian Languages (CIIL) from 1991 to 1995 [25].…”

Section: Bangla Language Corpusmentioning

confidence: 99%

Compilation, Analysis and Application of a Comprehensive Bangla Corpus KUMono

et al. 2022

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Research in Natural Language Processing (NLP) and computational linguistics highly depends on a good quality representative corpus of any specific language. Bangla is one of the most spoken languages in the world but Bangla NLP research is in its early stage of development due to the lack of quality public corpus. This article describes the detailed compilation methodology of a comprehensive monolingual Bangla corpus, KUMono. The newly developed corpus consists of more than 350 million word tokens and more than one million unique tokens from 18 major text categories of online Bangla websites. We have conducted several word-level and character-level linguistic phenomenon analyses based on empirical studies of the developed corpus. The corpus follows Zipf's curve and hapax legomena rule. The quality of the corpus is also assessed by analyzing and comparing the inherent sparseness of the corpus with existing Bangla corpora, by analyzing the distribution of function words of the corpus and vocabulary growth rate. We have developed a Bangla article categorization application based on the KUMono corpus and received compelling results by comparing to the state-of-the-art models.

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