A New Language-Independent Deep CNN for Scene Text Detection and Style Transfer in Social Media Images.

Due to the adverse effect of quality caused by different social media and arbitrary languages in natural scenes, detecting text from social media images and transferring its style is challenging. This paper presents a novel end-to-end model for text detection and text style transfer in social media images. The key notion of the proposed work is to find dominant information, such as fine details in the degraded images (social media images), and then restore the structure of character information. Therefore, we first introduce a novel idea of extracting gradients from the frequency domain of the input image to reduce the adverse effect of different social media, which outputs text candidate points. The text candidates are further connected into components and used for text detection via a UNet++ like network with an EfficientNet backbone (EffiUNet++). Then, to deal with the style transfer issue, we devise a generative model, which comprises a target encoder and style parameter networks (TESP-Net) to generate the target characters by leveraging the recognition results from the first stage. Specifically, a series of residual mapping and a position attention module are devised to improve the shape and structure of generated characters. The whole model is trained end-to-end so as to optimize the performance. Experiments on our social media dataset, benchmark datasets of natural scene text detection and text style transfer show that the proposed model outperforms the existing text detection and style transfer methods in multilingual and cross-language scenario.

A Conformable Moments-Based Deep Learning System for Forged Handwriting Detection.

Detecting forged handwriting is important in a wide variety of machine learning applications, and it is challenging when the input images are degraded with noise and blur. This article presents a new model based on conformable moments (CMs) and deep ensemble neural networks (DENNs) for forged handwriting detection in noisy and blurry environments. Since CMs involve fractional calculus with the ability to model nonlinearities and geometrical moments as well as preserving spatial relationships between pixels, fine details in images are preserved. This motivates us to introduce a DENN classifier, which integrates stenographic kernels and spatial features to classify input images as normal (original, clean images), altered (handwriting changed through copy-paste and insertion operations), noisy (added noise to original image), blurred (added blur to original image), altered-noise (noise is added to the altered image), and altered-blurred (blur is added to the altered image). To evaluate our model, we use a newly introduced dataset, which comprises handwritten words altered at the character level, as well as several standard datasets, namely ACPR 2019, ICPR 2018-FDC, and the IMEI dataset. The first two of these datasets include handwriting samples that are altered at the character and word levels, and the third dataset comprises forged International Mobile Equipment Identity (IMEI) numbers. Experimental results demonstrate that the proposed method outperforms the existing methods in terms of classification rate.