Multichannel Attention Networks with Ensembled Transfer Learning to Recognize Bangla Handwritten Charecter

• URL: http://arxiv.org/abs/2408.10955v1
• Date: Tue, 20 Aug 2024 15:51:01 GMT
• Title: Multichannel Attention Networks with Ensembled Transfer Learning to Recognize Bangla Handwritten Charecter
• Authors: Farhanul Haque, Md. Al-Hasan, Sumaiya Tabssum Mou, Abu Saleh Musa Miah, Jungpil Shin, Md Abdur Rahim,
• Abstract summary: The study employed a convolutional neural network (CNN) with ensemble transfer learning and a multichannel attention network. We evaluated the proposed model using the CAMTERdb 3.1.2 data set and achieved 92% accuracy for the raw dataset and 98.00% for the preprocessed dataset.
• Score: 1.5236380958983642
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Bengali language is the 5th most spoken native and 7th most spoken language in the world, and Bengali handwritten character recognition has attracted researchers for decades. However, other languages such as English, Arabic, Turkey, and Chinese character recognition have contributed significantly to developing handwriting recognition systems. Still, little research has been done on Bengali character recognition because of the similarity of the character, curvature and other complexities. However, many researchers have used traditional machine learning and deep learning models to conduct Bengali hand-written recognition. The study employed a convolutional neural network (CNN) with ensemble transfer learning and a multichannel attention network. We generated the feature from the two branches of the CNN, including Inception Net and ResNet and then produced an ensemble feature fusion by concatenating them. After that, we applied the attention module to produce the contextual information from the ensemble features. Finally, we applied a classification module to refine the features and classification. We evaluated the proposed model using the CAMTERdb 3.1.2 data set and achieved 92\% accuracy for the raw dataset and 98.00\% for the preprocessed dataset. We believe that our contribution to the Bengali handwritten character recognition domain will be considered a great development.

Related papers

• A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus [71.77214818319054]
  Natural language inference is a proxy for natural language understanding. There is no publicly available NLI corpus for the Romanian language. We introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs.
  arXiv  Detail & Related papers  (2024-05-20T08:41:15Z)
• BanglaNet: Bangla Handwritten Character Recognition using Ensembling of Convolutional Neural Network [0.0]
  This paper presents a classification model based on the ensembling of several Convolutional Neural Networks (CNN) Three different models based on the idea of state-of-the-art CNN models like Inception, ResNet, and DenseNet have been trained with both augmented and non-augmented inputs. Rigorous experimentation on three benchmark Bangla handwritten characters datasets, namely, CMATERdb, BanglaLekha-Isolated, and Ekush has exhibited significant recognition accuracies.
  arXiv  Detail & Related papers  (2024-01-16T01:08:19Z)
• Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages [70.25418443146435]
  We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data. We employ two independent datasets of SMSs in English and Arabic, each carrying semi-structured banking transaction information. With access to only 30 labeled samples, our model can generalize the recognition of merchants, amounts, and other fields from English to Arabic.
  arXiv  Detail & Related papers  (2023-07-16T00:45:42Z)
• Bengali Handwritten Digit Recognition using CNN with Explainable AI [0.5156484100374058]
  We have used various machine learning algorithms and CNN to recognize handwritten Bengali digits. Grad-CAM was used as an XAI method on our CNN model, which gave us insights into the model.
  arXiv  Detail & Related papers  (2022-12-23T04:40:20Z)
• Efficient approach of using CNN based pretrained model in Bangla handwritten digit recognition [0.0]
  Handwritten digit recognition is essential for numerous applications in various industries. Due to the complexity of Bengali writing in terms of variety in shape, size, and writing style, researchers did not get better accuracy usingSupervised machine learning algorithms to date. We propose a novel CNN-based pre-trained handwritten digit recognition model which includes Resnet-50, Inception-v3, and EfficientNetB0 on NumtaDB dataset of 17 thousand instances with 10 classes.
  arXiv  Detail & Related papers  (2022-09-19T15:58:53Z)
• Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self Attention [0.0]
  Our work explores how we can effectively use deep neural networks in transfer learning and joint dual input learning settings to effectively classify sentiments and detect hate speech in Hindi and Bengali data. We use BiLSTM with self attention in joint dual input learning setting where we train a single neural network on Hindi and Bengali dataset simultaneously using their respective embeddings.
  arXiv  Detail & Related papers  (2022-02-11T05:36:11Z)
• Utilizing Wordnets for Cognate Detection among Indian Languages [50.83320088758705]
  We detect cognate word pairs among ten Indian languages with Hindi. We use deep learning methodologies to predict whether a word pair is cognate or not. We report improved performance of up to 26%.
  arXiv  Detail & Related papers  (2021-12-30T16:46:28Z)
• Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages [50.82410844837726]
  We demonstrate the use of cross-lingual word embeddings for detecting cognates among fourteen Indian languages. We evaluate our methods to detect cognates on a challenging dataset of twelve Indian languages. We observe an improvement of up to 18% points, in terms of F-score, for cognate detection.
  arXiv  Detail & Related papers  (2021-12-16T11:17:58Z)
• Bengali Handwritten Grapheme Classification: Deep Learning Approach [0.0]
  We participate in a Kaggle competition citek_link where the challenge is to classify three constituent elements of a Bengali grapheme in the image. We explore the performances of some existing neural network models such as Multi-Layer Perceptron (MLP) and state of the art ResNet50. We propose our own convolution neural network (CNN) model for Bengali grapheme classification with validation root accuracy 95.32%, vowel accuracy 98.61%, and consonant accuracy 98.76%.
  arXiv  Detail & Related papers  (2021-11-16T06:14:59Z)
• Skeleton Based Sign Language Recognition Using Whole-body Keypoints [71.97020373520922]
  Sign language is used by deaf or speech impaired people to communicate. Skeleton-based recognition is becoming popular that it can be further ensembled with RGB-D based method to achieve state-of-the-art performance. Inspired by the recent development of whole-body pose estimation citejin 2020whole, we propose recognizing sign language based on the whole-body key points and features.
  arXiv  Detail & Related papers  (2021-03-16T03:38:17Z)
• Soft Gazetteers for Low-Resource Named Entity Recognition [78.00856159473393]
  We propose a method of "soft gazetteers" that incorporates ubiquitously available information from English knowledge bases into neural named entity recognition models. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score.
  arXiv  Detail & Related papers  (2020-05-04T21:58:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.