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

A CNN Based Framework for Unistroke Numeral Recognition in Air-Writing

Air-writing refers to virtually writing linguistic characters through hand gestures in three-dimensional space with six degrees of freedom. This paper proposes a generic video camera-aided convolutional neural network (CNN) based air-writing framework. Gestures are performed using a marker of fixed color in front of a generic video camera, followed by color-based segmentation to identify the marker and track the trajectory of the marker tip. A pre-trained CNN is then used to classify the gesture. The recognition accuracy is further improved using transfer learning with the newly acquired data. The performance of the system varies significantly on the illumination condition due to color-based segmentation. In a less fluctuating illumination condition, the system is able to recognize isolated unistroke numerals of multiple languages. The proposed framework has achieved 97.7%, 95.4% and 93.7% recognition rates in person independent evaluations on English, Bengali and Devanagari numerals, respectively.Comment: Accepted in The International Conference on Frontiers of Handwriting Recognition (ICFHR) 201

Validating a novel angular power spectrum estimator using simulated low frequency radio-interferometric data

The "Tapered Gridded Estimator" (TGE) is a novel way to directly estimate the angular power spectrum from radio-interferometric visibility data that reduces the computation by efficiently gridding the data, consistently removes the noise bias, and suppresses the foreground contamination to a large extent by tapering the primary beam response through an appropriate convolution in the visibility domain. Here we demonstrate the effectiveness of TGE in recovering the diffuse emission power spectrum through numerical simulations. We present details of the simulation used to generate low frequency visibility data for sky model with extragalactic compact radio sources and diffuse Galactic synchrotron emission. We then use different imaging strategies to identify the most effective option of point source subtraction and to study the underlying diffuse emission. Finally, we apply TGE to the residual data to measure the angular power spectrum, and assess the impact of incomplete point source subtraction in recovering the input power spectrum $C_{\ell}$ of the synchrotron emission. This estimator is found to successfully recovers the $C_{\ell}$ of input model from the residual visibility data. These results are relevant for measuring the diffuse emission like the Galactic synchrotron emission. It is also an important step towards characterizing and removing both diffuse and compact foreground emission in order to detect the redshifted $21\, {\rm cm}$ signal from the Epoch of Reionization.Comment: 18 pages, 1 table, 9 figures, Accepted for publication in New Astronom

Ultrasonographic evaluation of anatomical variations, of the first dorsal compartment of wrist, and its correlation with de Quervain’s disease recurrence in Indian population: a hospital based longitudinal study

Background: de Quervain’s disease is a commonly encountered debilitating condition mostly treated conservatively. Cadaveric studies have revealed anatomical variations in the first dorsal compartment of the wrist; however, a causal association between the variants and recurrence of the disease has not been established. An ultrasonographic assessment of the first dorsal compartment of the wrist was done, to correlate the response to treatment, of the different anatomical variants. Methods: 106 wrists were included in the study after clinically confirming de Quervain’s disease. All were injected with 20mg of methylprednisolone acetate in the first dorsal compartment of the wrist and were followed up. Patients who were pain free for at least six months, as well as patients who did not respond or had a recurrence of disease within eight to twelve weeks, were all assessed with ultrasound. Results: 57.54% of the wrists were cured with one injection most of which revealed a tendon arrangement of one abductor pollicis longus (APL) and one extensor pollicis brevis (EPB) and two APL and one EPB without any septation. 32.07% of the wrists did not respond to this treatment and most of them comprised of septations and aberrant compartments with presence of supernumerary tendons. Conclusions: Ultrasonographic assessment of the first dorsal compartment of wrist, of all patients suffering from de Quervain’s tenosynovitis must be done to predict the outcome of conservative therapy and customize an optimal treatment modality for the individual anatomical variant

Quantitative Risk Assessment in Titanium Sponge Plant

This pap& presents the quantitative risk assessment for the storage of titanium tetrachloride (TiCl,).It is the major reactant used for the production of titanium in the titanium spongeplant. Titanium tetrachloride readily reacts with moisture, leading to the release of toxic hydrogen chloride (HCI).F ire explosive and toxicity index analysis, and hazard and operability(HAZOP) studies for the entire titanium sponge plant were carried out. Based on these studies, the TiCl, storage section was found to be one of the most hazardous sections in the titaniumsponge plant. Fault tree analysis technique has been used to identify the basic events responsible for the top event occurrence, ie, release of HCl due to the hydrolysis of TiCl, upon contactwith moisture in the environment during spillagelleakage of TiCl, from the storage tanks and to calculate its probability. Consequence analysis of the probable scenarios has been carriedout. The risk has been estimated in terms of fatality.and injuries. Based on these results, basic input in the form of recommendations for possible changes in the design and operation of thetitanium sponge plant have been made for the risk management

Effects of Degradations on Deep Neural Network Architectures

Recently, image classification methods based on capsules (groups of neurons) and a novel dynamic routing protocol are proposed. The methods show promising performances than the state-of-the-art CNN-based models in some of the existing datasets. However, the behavior of capsule-based models and CNN-based models are largely unknown in presence of noise. So it is important to study the performance of these models under various noises. In this paper, we demonstrate the effect of image degradations on deep neural network architectures for image classification task. We select six widely used CNN architectures to analyse their performances for image classification task on datasets of various distortions. Our work has three main contributions: 1) we observe the effects of degradations on different CNN models; 2) accordingly, we propose a network setup that can enhance the robustness of any CNN architecture for certain degradations, and 3) we propose a new capsule network that achieves high recognition accuracy. To the best of our knowledge, this is the first study on the performance of CapsuleNet (CapsNet) and other state-of-the-art CNN architectures under different types of image degradations. Also, our datasets and source code are available publicly to the researchers.Comment: Journa