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.

One of the major causes of poor results in license plate recognition is low quality of images affected by multiple factors, such as severe illumination condition, complex background, different weather conditions, night light, and perspective distortions. In this paper, we propose a new mathematical model based on Riesz fractional operator for enhancing details of edge information in license plate images to improve the performances of text detection and recognition methods. The proposed model performs convolution operation of the Riesz fractional derivative over each input image by enhancing the edge strength in it. To test the performance of the proposed model, we conduct experiments on benchmark license plate image databases, namely, UCSD and ICDAR 2015-SR competition text image databases. Experimental results on enhancement show that the proposed model outperforms the existing baseline enhancement techniques in terms of quality measures. Furthermore, experimental results on text detection and recognition show that text detection and recognition rates are improved significantly after enhancement compared with before enhancement.