STEFANN: Scene Text Editor using Font Adaptive Neural Network

Textual information in a captured scene plays an important role in scene interpretation and decision making. Though there exist methods that can successfully detect and interpret complex text regions present in a scene, to the best of our knowledge, there is no significant prior work that aims to modify the textual information in an image. The ability to edit text directly on images has several advantages including error correction, text restoration and image reusability. In this paper, we propose a method to modify text in an image at character-level. We approach the problem in two stages. At first, the unobserved character (target) is generated from an observed character (source) being modified. We propose two different neural network architectures - (a) FANnet to achieve structural consistency with source font and (b) Colornet to preserve source color. Next, we replace the source character with the generated character maintaining both geometric and visual consistency with neighboring characters. Our method works as a unified platform for modifying text in images. We present the effectiveness of our method on COCO-Text and ICDAR datasets both qualitatively and quantitatively.Comment: Accepted in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 202

Regularized Evolutionary Algorithm for Dynamic Neural Topology Search

Designing neural networks for object recognition requires considerable architecture engineering. As a remedy, neuro-evolutionary network architecture search, which automatically searches for optimal network architectures using evolutionary algorithms, has recently become very popular. Although very effective, evolutionary algorithms rely heavily on having a large population of individuals (i.e., network architectures) and is therefore memory expensive. In this work, we propose a Regularized Evolutionary Algorithm with low memory footprint to evolve a dynamic image classifier. In details, we introduce novel custom operators that regularize the evolutionary process of a micro-population of 10 individuals. We conduct experiments on three different digits datasets (MNIST, USPS, SVHN) and show that our evolutionary method obtains competitive results with the current state-of-the-art

Unveiling Neutrino Mysteries with Δ(27)\Delta(27) Symmetry

In the realm of neutrino physics, we grapple with mysteries like the origin of neutrino masses, the absence of a clear mass hierarchy, and the values of Majorana phases. To address these puzzles, we extend the Standard Model using $\Delta(27)$ symmetry within the Hybrid seesaw framework. We also introduce an additional $Z_{10}$ symmetry to constrain some undesirable terms in the Yukawa Lagrangian, resulting in a unique neutrino mass matrix texture with partial $\mu-\tau$ symmetry. In our study, we propose a novel lepton mixing matrix that, when connected to this texture, provides valuable phenomenological insights

A Realistic Neutrino mixing scheme arising from A4A_4 symmetry

We propose a unique lepton mixing scheme and its association with an exact hierarchy-philic neutrino mass matrix texture in the light of a hybrid type seesaw mechanism under the framework of $A_4 \times Z_3 \times Z_{10}$ discrete flavour symmetry

Leptons and other forces of Nature

Assuming that the neutrinos are not Majorana particles and the righthanded Diarc neutrinos do not exist, we propose that all the three flavour neutrinos are not elementary. We posit that the electron and the electron type neutrino are fundamental particles, while the other members of the lepton family are composite states. In this regard, two complex hidden scalar fields and two new hidden fundamental forces are introduced. The gauge symmetry $SU(2)_L \otimes U(1)_Y \otimes SU(2)_h \otimes U(1)_h$ describing the Electroweak and the Hidden forces, breaks down to $U(1)_Q\otimes U(1)_h$ after the Higgs mechanism