An empirical study on writer identification and verification from intra-variable individual handwriting

© 2013 IEEE. The handwriting of a person may vary substantially with factors, such as mood, time, space, writing speed, writing medium/tool, writing a topic, and so on. It becomes challenging to perform automated writer verification/identification on a particular set of handwritten patterns (e.g., speedy handwriting) of an individual, especially when the system is trained using a different set of writing patterns (e.g., normal speed) of that same person. However, it would be interesting to experimentally analyze if there exists any implicit characteristic of individuality which is insensitive to high intra-variable handwriting. In this paper, we study some handcrafted features and auto-derived features extracted from intra-variable writing. Here, we work on writer identification/verification from highly intra-variable offline Bengali writing. To this end, we use various models mainly based on handcrafted features with support vector machine and features auto-derived by the convolutional network. For experimentation, we have generated two handwritten databases from two different sets of 100 writers and enlarged the dataset by a data-augmentation technique. We have obtained some interesting results

WRITER IDENTIFICATION BY TEXTURE ANALYSIS BASED ON KANNADA HANDWRITING

Writer identification problem is one of the important area of research due to its various applications and is a challenging task. The major research on writer identification is based on handwritten English documents with text independent and dependent. However, there is no significant work on identification of writers based on Kannada document. Hence, in this paper, we propose a text-independent method for off-line writer identification based on Kannada handwritten scripts. By observing each individual’s handwriting as a different texture image, a set of features based on Discrete Cosine Transform, Gabor filtering and gray level co-occurrence matrix, are extracted from preprocessed document image blocks. Experimental results demonstrate that the Gabor energy features are more potential than the DCTs and GLCMs based features for writer identification from 20 people

Managing interactions between technological and stylistic innovation in the media industries, insights from the introduction of ebook technology in the publishing industry

The mainstream of innovation research pays a lot of attention to technological innovation, but has neglected its interaction with another type of innovation, which is particularly important in sectors like the furniture, fashion and the media content industries: stylistic innovation. This paper explains how the quality certification processes for technological and stylistic innovations differ and how they may interact in the media industries. Awards are discussed as specific instantiations of micro certification schemes indicating excellence with respect to stylistic and/or technological product features. Furthermore, a definition of stylistic innovation is developed with reference to organizational identity as well as reputation, two key concepts, which permeate the processes of innovation and certification discussed in this paper. Stylistic and technological innovation may take place in both, the content as well as the form of media products. It will be argued that the interaction between stylistic and technological innovation depends, first of all, on the location of each of these types of innovation within the product, and, secondly, on the characteristics of the certification scheme faced by the producing firms. Within the media sector the literary publishing industry has been chosen to provide the subject of the empirical part. Two case studies related to the introduction of eBook technology are presented: One is a study of the first digital literary publisher in Europe and the other is a case study of the first international eBook award, which mixes technological and stylistic criteria. Theory and cases lead to a number of hypotheses, which are offered as potential departure points for future research on the interaction between innovation in style and technology.awards;certification;media industries;stylistic innovation;technological innovation

Multilingual Fake News Detection with Satire

International audienceThe information spread through the Web influences politics, stock markets, public health, people's reputation and brands. For these reasons, it is crucial to filter out false information. In this paper, we compare different automatic approaches for fake news detection based on statistical text analysis on the vaccination fake news dataset provided by the Storyzy company. Our CNN works better for discrimination of the larger classes (fake vs trusted) while the gradient boosting decision tree with feature stacking approach obtained better results for satire detection. We contribute by showing that efficient satire detection can be achieved using merged embeddings and a specific model, at the cost of larger classes. We also contribute by merging redundant information on purpose in order to better predict satire news from fake news and trusted news

LT3 at SemEval-2018 Task 1 : a classifier chain to detect emotions in tweets

This paper presents an emotion classification system for English tweets, submitted for the SemEval shared task on Affect in Tweets, subtask 5: Detecting Emotions. The system combines lexicon, n-gram, style, syntactic and semantic features. For this multi-class multi-label problem, we created a classifier chain. This is an ensemble of eleven binary classifiers, one for each possible emotion category, where each model gets the predictions of the preceding models as additional features. The predicted labels are combined to get a multi-label representation of the predictions. Our system was ranked eleventh among thirty five participating teams, with a Jaccard accuracy of 52.0% and macro- and micro-average F1-scores of 49.3% and 64.0%, respectively