Browsing by Author "Akter, Tapsara"

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Recognizing and Understanding Handwritten Joint Letter in Bangla
(Daffodil International University, 2019-12-08) Akter, Tapsara
Recently, some work has been done on Bangla Handwriting character recognition. But many of them are only capable of recognizing Bangla digits, vowel or consonant character only. For building a model that can recognize Bangla handwriting, it is important to work with joint letter also. This “Recognizing & understanding handwritten joint letter in Bangla” is a project to develop a model to recognize and understand Bangla handwritten joint letters. This model will be capable of taking single or multiple images as input and then it will automatically recognize which joint letter is in the image. For this project I have collected the dataset from multiple sources. In this project, I have used transfer learning with deep learning for getting best result. Here I implemented some Deep Neural Network (DNN) and compared the results of them. Among all of them ResNet50 provides the best result. The result of the model showed 96.37% accuracy on testing dataset which is better than other existing works. Keywords — Convolutional Neural Network, Transfer Learning, Handwritten Joint Letter Recognition, Handwritten Joint Letters.
Recognizing and Understanding Handwritten Joint Letter in Bangla
(Daffodil International University, 2019-12-08) Akter, Tapsara
Recently, some work has been done on Bangla Handwriting character recognition. But many of them are only capable of recognizing Bangla digits, vowel or consonant character only. For building a model that can recognize Bangla handwriting, it is important to work with joint letter also. This “Recognizing & understanding handwritten joint letter in Bangla” is a project to develop a model to recognize and understand Bangla handwritten joint letters. This model will be capable of taking single or multiple images as input and then it will automatically recognize which joint letter is in the image. For this project I have collected the data set from multiple sources. In this project, I have used transfer learning with deep learning for getting best result. Here I implemented some Deep Neural Network (DNN) and compared the results of them. Among all of them ResNet50 provides the best result. The result of the model showed 96.37% accuracy on testing data set which is better than other existing works. Keywords — Convolutional Neural Network, Transfer Learning, Handwritten Joint Letter Recognition, Handwritten Joint Letters.
Recognizing Bangladeshi Agricultural Insects Using Machine Learning
(Daffodil International University, 23-01-18) Akter, Tapsara
Recently, some work has been done on agricultural insect recognition. But a limited number of works is done on Bangladeshi agricultural insects. Pests decimate crops on a massive scale each year. To achieve high crop output, pest detection and identification are necessary. For efficient pest control management, early pest detection in photographs is absolutely essential. Therefore, it has been difficult to identify the pest in the picture. I gathered the dataset for this study from a variety of sources. To achieve the greatest results in this study, I combined deep learning and transfer learning. I used some Deep Neural Networks here (DNN). ResNet50 and VGG16 produce the greatest results out of all of them. The model's output demonstrated 96.4% accuracy on the testing dataset, which is superior to other previous works. Keywords — Convolutional Neural Network, Transfer Learning, Bangladeshi Agricultural insect Recognition, Bangladeshi Agricultural insects.