Deep learning apporach for image captioning in Hindi language

Rathi, Ankit

doi:10.1109/iccece48148.2020.9223087

Cited by 12 publications

(5 citation statements)

References 28 publications

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“…The study examines multiple LSTM and Bi-LSTM models, with VGG16 using a bi-LSTM model earning the highest BLUE1 score of 0.583. The author [77] focuses on the production of image captions in artificial intelligence, specifically in Hindi. The project trains an encoder-decoder neural network model using Flickr8k-Hindi Datasets, a Hindi picture description dataset.…”

Section: Image Captioning For the Hindi Languagementioning

confidence: 99%

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A Comprehensive Survey on Image Captioning for Indian Languages: Techniques, Datasets, and Challenges

Jayaswal,

Rani,

Kaur

2023

Preprint

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In image captioning, we generate visual descriptions from an image. Image Cap-tioning requires identifying the key entity, feature, and association in an image. There is also a requirement to generate captions that are syntactically and semantically correct. The process of image captioning requires computer vision and natural language processing. In the past few decades, a substantial attempt has been made to generate the caption for images. In this survey article, we are going to present an extensive survey on image captioning for Indian Languages. To summarize recent research work in image captioning, first, we briefly review the traditional approach to image captioning depending on template and retrieval. Further deep-learning approaches for image captioning are concentrated which are classified as encoder-decoder architecture, attention-based approach, and transformer architecture. Our main focus in this survey is based on image cap-tioning techniques for Indian languages like Hindi, Bengali Assamese, etc. After that, we analyze the state-of-the-art approach on the most widely dataset i.e. MS COCO dataset with their strengths, limitations, and performance metrics i.e. BLEU, ROUGE, METEOR, CIDEr, SPICE. At last, we explore discussion on open challenges and future direction in the field of image captioning.

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Section: Image Captioning For the Hindi Languagementioning

confidence: 99%

“…The author [77] seeks to solve the scarcity of image captioning datasets for morphologically rich languages such as Hindi. It creates a Hindi image caption dataset with Flickr8k-Hindi Datasets, which includes four datasets based on picture descriptions and clean or unclean descriptions.…”

Section: Image Captioning For the Hindi Languagementioning

confidence: 99%

A Comprehensive Survey on Image Captioning for Indian Languages: Techniques, Datasets, and Challenges

Jayaswal,

Rani,

Kaur

2023

Preprint

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“…Even though it is an image captioning data set, we discard the images, treating the data set as in-domain monolingual data. We use a machine translation into Hindi (Rathi, 2020) as the target side, and generate image features using the procedure described in Section 3.4.…”

Section: Data Setsmentioning

confidence: 99%

Silo NLP's Participation at WAT2022

Parida¹,

Panda²,

Grönroos³

et al. 2022

Preprint

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This paper provides the system description of "Silo NLP's" submission to the Workshop on Asian Translation (WAT2022). We have participated in the Indic Multimodal tasks (English→Hindi, English→Malayalam, and English→Bengali, Multimodal Translation). For text-only translation, we trained Transformers from scratch and fine-tuned mBART-50 models. For multimodal translation, we used the same mBART architecture, and extracted object tags from the images to use as visual features concatenated with the text sequence.Our submission tops many tasks including English→Hindi multimodal translation (evaluation test), English→Malayalam textonly and multimodal translation (evaluation test), English→Bengali multimodal translation (challenge test), and English→Bengali text-only translation (evaluation test).cz/bengali-visual-genome/ wat-2022-english-bengali-multim 2 All except the EN-HI and EN-ML text-only systems.3 https://lindat.mff.cuni.cz/ repository/xmlui/handle/11234/1-3267

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“…The results from this paper reveal that neural machine translation system provides with a high accuracy result but still needs to be assessed in the future. [2], it is seen that the model used in this paper is trained to predict the image caption using the image feature vector and the previous word. This paper has tested their model on four databases and concluded that image definition quality increases after training the model with a pure database.…”

Section: Literature Surveymentioning

confidence: 99%

Machine Translation Systems for English Captions to Hindi Language Using Deep Learning

Singh,

Bhase,

Jain

et al. 2022

ITM Web Conf.

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Machine Translation is the process of translating text from one language to another which helps to reduce the conversation gap among people from different cultural backgrounds. The task performed by the Machine Translation System is to automatically translate between pairs of different natural languages, where Neural Machine Translation System stands out from all because it provides fluent translation along with reasonable translation accuracy. The Convolution Neural Network encoder is used to find patterns in the images and encode it into a vector that is passed to the Long Short Term Memory decoder which finds the caption word-by-word to best describe the image. Upon reaching the end-line token, the entire description of the image in English is generated and that is our output for that particular image. Automatically creating the description of an image in English using any natural language sentences and then translating it using Neural Machine Translation to Hindi is a very challenging task. It requires expertise in both image processing as well as natural language processing. In this paper, the aim is to compare the two Machine Translation Systems: Google Translation System and the proposed Neural Machine Translation System to convert the text obtained from an image in English to Hindi language.

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Deep learning apporach for image captioning in Hindi language

Cited by 12 publications

References 28 publications

A Comprehensive Survey on Image Captioning for Indian Languages: Techniques, Datasets, and Challenges

A Comprehensive Survey on Image Captioning for Indian Languages: Techniques, Datasets, and Challenges

Silo NLP's Participation at WAT2022

Machine Translation Systems for English Captions to Hindi Language Using Deep Learning

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