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A new multimodal sentiment analysis for images containing textual information

Multimedia Tools and Applications 2024年 1-30页

作者： Ahuja, Garvit Alaei, Alireza Pal, Umapada School of Computer Science and Engineering Manipal University Jaipur Jaipur India Faculty of Science and Engineering Southern Cross University Gold Coast Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Multimodal sentiment analysis on images with textual content is a research area aiming to understand the sentiment conveyed by visual and textual elements in the images. While multimodal sentiment analysis on images and text (reviews) has its own challenges, the combination of textual and visual content in the form of images presents new challenges as well as opportunities. In this research work, we proposed a multimodal sentiment analysis method that works on images incorporating textual elements. In the textual sentiment analysis model, we initially employed a recognition system to extract textual data from input images. Our proposed multimodal method is based on transfer learning, considering two pre-trained deep learning models, Xception, and RoBERTa, to extract features from both visual and textual content from multimedia images. We then implemented a fusion strategy to combine these two modalities (Visual Sentiment Analysis (VSA) and Textual Sentiment Analysis (TSA)) to enhance the accuracy of the proposed method and to provide a more comprehensive understanding of sentiment in multimedia content. In addition, we curated a custom dataset comprising images with associated text labels and sentiments. To ensure accurate labels, we conducted human evaluations involving thirty annotators. Our dataset includes images labeled with negative, neutral, and positive sentiments. Experimental results demonstrated the effectiveness of combining visual and textual features for sentiment analysis. The findings from this research hold promising implications for real-world applications, such as sentiment analysis in social media, product reviews, and marketing campaigns, where both images and text play a significant role in conveying emotional context. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

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Segmentation of Bangla handwritten text into characters by recursive contour following 5

Segmentation of Bangla handwritten text into characters by r...

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5th International Conference on Document Analysis and recognition, ICDAR 1999

作者： Bishnu, A. Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Calcutta700035 India

ISBN: (纸本)0769503187

Segmentation of handwritten words into characters is one of the important components in handwritten text OCR. In this paper we put forward a method for the segmentation of handwritten Bangla (an Indo-Bangladeshi language) text into characters. Based on certain characteristics of Bangla writing methods, different zones across the height of the word are detected. These zones provide certain structural information about the constituent characters of the respective word. In Bangla handwritten texts often there is overlap between rectangular hulls of successive characters. As such the characters are seldom vertically separable. So, we propose a method of recursive contour following in one of the zones across the height of the word to find out the extents within which the main portion of the character lies. If the successive characters are not touching in the zone of contour following, the algorithm gives fairly good results. © 1999 IEEE.

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CNN based common approach to handwritten character recognition of multiple scripts 13

CNN based common approach to handwritten character recogniti...

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13th International Conference on Document Analysis and recognition, ICDAR 2015

作者： Maitra, Durjoy Sen Bhattacharya, Ujjwal Parui, Swapan K. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata-108 India

ISBN: (纸本)9781479918058

There are many scripts in the world, several of which are used by hundreds of millions of people. Handwritten character recognition studies of several of these scripts are found in the literature. Different hand-crafted feature sets have been used in these recognition studies. However, convolutional neural network (CNN) has recently been used as an efficient unsupervised feature vector extractor. Although such a network can be used as a unified framework for both feature extraction and classification, it is more efficient as a feature extractor than as a classifier. In the present study, we performed certain amount of training of a 5-layer CNN for a moderately large class character recognition problem. We used this CNN trained for a larger class recognition problem towards feature extraction of samples of several smaller class recognition problems. In each case, a distinct Support Vector Machine (SVM) was used as the corresponding classifier. In particular, the CNN of the present study is trained using samples of a standard 50-class Bangla basic character database and features have been extracted for 5 different 10-class numeral recognition problems of English, Devanagari, Bangla, Telugu and Oriya each of which is an official indian script. recognition accuracies are comparable with the state-of-the-art. © 2015 IEEE.

关键词： Character recognition

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Text detection of two major indian scripts in natural scene images

Text detection of two major Indian scripts in natural scene ...

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4th International Workshop on Camera-Based Document Analysis and recognition, CBDAR 2011

作者： Roy Chowdhury, Aruni Bhattacharya, Ujjwal Parui, Swapan K. Department of Information Technology Heritage Institute of Technology Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9783642293634

In this article, we present a robust scheme for detection of Devanagari or Bangla texts in scene images. These are the two most popular scripts in India. The proposed scheme is primarily based on two major characteristics of such texts - (i) variations in stroke thickness for text components of a script are low compared to their non-text counterparts and (ii) presence of a headline along with a few vertical downward strokes originating from this headline. We use the Euclidean distance transform to verify the general characteristics of texts in (i). Also, we apply the probabilistic Hough line transform to detect the characteristic headline of Devanagari and Bangla texts. Further, similarity and adjacency measures are applied to identify text regions, which do not satisfy the verification in (ii). The proposed approach has been simulated on a repository of 120 images taken from indian roads and the results are encouraging. Also, we have discussed the applicability of the proposed scheme for detection of English texts. Towards this end, we have considered the training and test samples from the image database of ICDAR 2003 Robust Reading Competition. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Character recognition

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Script line separation from indian multi-script documents 5

Script line separation from Indian multi-script documents

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5th International Conference on Document Analysis and recognition, ICDAR 1999

作者： Pal, U. Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Calcutta35 India

ISBN: (纸本)0769503187

In a multi-lingual country like India, a document page may contain more than one script form. Under the three-language formula, the document may be printed in English, Devnagari and one of the other official indian languages. For OCR of such a document page, it is necessary to separate these three script forms before feeding them to the OCRs of individual scripts. In this paper, an automatic technique of separating the text lines using script characteristics and shape based features is presented. At present, the system has an overall accuracy of about 98.5%. © 1999 IEEE.

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Continuous Motion Numeral recognition Using RNN Architecture in Air-Writing Environment 5th

Continuous Motion Numeral Recognition Using RNN Architecture...

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5th Asian Conference on pattern recognition, ACPR 2019

作者： Rahman, Adil Roy, Prasun Pal, Umapada Department of Information Technology Heritage Institute of Technology Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9783030414030

Air-writing, defined as character tracing in a three dimensional free space through hand gestures, is the way forward for peripheral-independent, virtual interaction with devices. While single unistroke character recognition is fairly simple, continuous writing recognition becomes challenging owing to absence of delimiters between characters. Moreover, stray hand motion while writing adds noise to the input, making accurate recognition difficult. The key to accurate recognition of air-written characters lies in noise elimination and character segmentation from continuous writing. We propose a robust and hardware-independent framework for multi-digit unistroke numeral recognition in air-writing environment. We present a sliding window based method which isolates a small segment of the spatio-temporal input from the air-writing activity for noise removal and digit segmentation. Recurrent Neural Networks (RNN) show great promise in dealing with temporal data and is the basis of our architecture. recognition of digits which have other digits as their sub-shapes is challenging. Capitalizing on how digits are commonly written, we propose a novel priority scheme to determine digit precedence. We only use sequential coordinates as input, which can be obtained from any generic camera, making our system widely accessible. Our experiments were conducted on English numerals using a combination of MNIST and Pendigits datasets along with our own air-written English numerals dataset (ISI-Air Dataset). Additionally, we have created a noise dataset to classify noise. We observe a drop in accuracy with increase in the number of digits written in a single continuous motion because of noise generated between digit transitions. However, under standard conditions, our system produced an accuracy of 98.45% and 82.89% for single and multiple digit English numerals, respectively. © Springer Nature Switzerland AG 2020.

关键词： Recurrent neural networks

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Text independent writer identification for Oriya script

Text independent writer identification for Oriya script

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10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Chanda, Sukalpa Franke, Katrin Pal, Umapada Department of Computer Science and Media Technology Gjøvik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9780769546612

Automatic identification of an individual based on his/her handwriting characteristics is an important forensic tool. In a computational forensic scenario, presence of huge amount of text/information in a questioned document cannot be ensured. Lack of data threatens system reliability in such cases. We here propose a writer identification system for Oriya script which is capable of performing reasonably well even with small amount of text. Experiments with curvature feature are reported here, using Support Vector Machine (SVM) as classifier. We got promising results of 94.00% writer identification accuracy at first top choice and 99% when considering first three top choices. © 2012 IEEE.

关键词： Support vector machines

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Automatic separation of machine-printed and hand-written text lines 5

Automatic separation of machine-printed and hand-written tex...

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5th International Conference on Document Analysis and recognition, ICDAR 1999

作者： Pal, U. Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Calcutta700 035 India

ISBN: (纸本)0769503187

There are many types of documents where machine-printed and hand-written texts appear intermixed. Since the optical character recognition (OCR) methodologies for machine-printed and hand-written texts are different, it is necessary to separate these two types of text before feeding them to the respective OCR systems. In this paper, we present such a scheme for both Bangla and Devnagari characters. The scheme is based on the structural and statistical features of the machine-printed and hand-written text lines. The classification scheme has an accuracy of about 98.3%. © 1999 IEEE.

关键词： Optical character recognition

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On OCR of degraded documents using fuzzy multifactorial analysis 5th

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5th International Conference on Asian Fuzzy Systems Society, AFSS 2002

作者： Garain, U. Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata700 035 India

ISBN: (纸本)9783540431503

Optical Character recognition (OCR) systems show poor performance while processing documents like old books or newspapers, Xerox materials, faxed documents, etc. Such documents are considered as degraded documents. One of the important reasons for poor recognition rate for degraded documents is existence of touching or connected characters, which create a major problem for designing an effective character segmentation procedure. In this paper, a new technique is proposed for segmentation of touching characters. The technique is based on fuzzy multifactorial analysis. A predictive algorithm is developed for effectively selecting cut-points to segment touching characters. Initially, our proposed method has been applied for segmenting touching characters that appear in Devnagari (Hindi) and Bangla, two major scripts in indian sub-continent. The results obtained from a test-set of considerable size show that a high recognition rate can be achieved with a reasonable amount of computations. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Optical character recognition

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An approach for processing mathematical expressions in printed document 3rd

An approach for processing mathematical expressions in print...

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3rd IAPR Workshop on Document Analysis Systems, DAS 1998

作者： Chaudhuri, B.B. Gamin, U. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Calcutta700 035 India

ISBN: (纸本)3540665072

In this paper, we propose an approach for understanding mathematical expressions in printed document. The system consists of three main components namely (i) detection of mathematical expressions in a document, (ii) recognition of the symbols present in the expression and (iii) meaningful arrangement of the recognized symbols. However, detection of mathematical expressions is done through recognition of symbols. Moreover, some structural features of the expressions are also used for this purpose. For recognition of the symbols a hybrid of feature based and template based recognition techniques is used. The bounding-box coordinates and the size information of the symbols help to determine the spatial relationships among the symbols. A set of predefined grammar rules is used to form the meaningful symbol groups to properly arrange the symbols. Experiments conducted using these approaches on a large number of documents show high accuracy. © Springer-Verlag Berlin Heidelberg 1999.

关键词： computers

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