Image to Bengali Caption Generation Using Deep CNN and Bidirectional Gated Recurrent Unit
There is very little notable research on generating descriptions of the Bengali language. About 243 million people speak in Bengali, and it is the 7th most spoken language on the planet. The purpose of this research is to propose a CNN and Bidirectional GRU based architecture model that generates natural language captions in the Bengali language from an image. Bengali people can use this research to break the language barrier and better understand each other's perspectives. It will also help many blind people with their everyday lives. This paper used an encoder-decoder approach to generate captions. We used a pre-trained Deep convolutional neural network (DCNN) called InceptonV3image embedding model as the encoder for analysis, classification, and annotation of the dataset's images Bidirectional Gated Recurrent unit (BGRU) layer as the decoder to generate captions. Argmax and Beam search is used to produce the highest possible quality of the captions. A new dataset called BNATURE is used, which comprises 8000 images with five captions per image. It is used for training and testing the proposed model. We obtained BLEU-1, BLEU-2, BLEU-3, BLEU-4 and Meteor is 42.6, 27.95, 23, 66, 16.41, 28.7 respectively.
Code (0)
등록된 구현이 없습니다.
Tasks
Caption GenerationDecoderImage CaptioningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Bornon: Bengali Image Captioning with Transformer-based Deep learning approach
Image captioning using Encoder-Decoder based approach where CNN is used as the Encoder and sequence generator like RNN as Decoder has proven to be very effective. However, this method has a drawback that is sequence need…
DecoderDeep LearningImage CaptioningBangla Image Caption Generation through CNN-Transformer based Encoder-Decoder Network
Automatic Image Captioning is the never-ending effort of creating syntactically and validating the accuracy of textual descriptions of an image in natural language with context. The encoder-decoder structure used through…
Caption GenerationDecoderImage CaptioningContext Sensitive Lemmatization Using Two Successive Bidirectional Gated Recurrent Networks
We introduce a composite deep neural network architecture for supervised and language independent context sensitive lemmatization. The proposed method considers the task as to identify the correct edit tree representing …
AttributeLEMMALemmatizationVocal Bursts Valence PredictionImage Captioning with Deep Bidirectional LSTMs
This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN) and two separate LSTM networks. It is …
Caption GenerationData AugmentationImage Captioningobject-detection+4Improved Bengali Image Captioning via deep convolutional neural network based encoder-decoder model
Image Captioning is an arduous task of producing syntactically and semantically correct textual descriptions of an image in natural language with context related to the image. Existing notable pieces of research in Benga…
DecoderImage Captioning