LoG and Structural Based Arbitrary Oriented Multilingual Text Detection in Images/Video

2018 ◽  
Vol 7 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Basavaraju H. T. ◽  
Manjunath Aradhya V.N. ◽  
Guru D. S. ◽  
Harish H. B. S.

Text in an image or a video affords more precise meaning and text is a prominent source with a clear explanation of the content than any other high-level or low-level features. The text detection process is a still challenging research work in the field of computer vision. However, complex background and orientation of the text leads to extremely stimulating text detection tasks. Multilingual text consists of different geometrical shapes than a single language. In this article, a simple and yet effective approach is presented to detect the text from an arbitrary oriented multilingual image and video. The proposed method employs the Laplacian of Gaussian to identify the potential text information. The double line structure analysis is applied to extract the true text candidates. The proposed method is evaluated on five datasets: Hua's, arbitrarily oriented, multi-script robust reading competition (MRRC), MSRA and video datasets with performance measures precision, recall and f-measure. The proposed method is also tested on real-time video, and the result is promising and encouraging.

2022 ◽  
pp. 987-1003
Author(s):  
H. T. Basavaraju ◽  
V.N. Manjunath Aradhya ◽  
D. S. Guru ◽  
H. B. S. Harish

Text in an image or a video affords more precise meaning and text is a prominent source with a clear explanation of the content than any other high-level or low-level features. The text detection process is a still challenging research work in the field of computer vision. However, complex background and orientation of the text leads to extremely stimulating text detection tasks. Multilingual text consists of different geometrical shapes than a single language. In this article, a simple and yet effective approach is presented to detect the text from an arbitrary oriented multilingual image and video. The proposed method employs the Laplacian of Gaussian to identify the potential text information. The double line structure analysis is applied to extract the true text candidates. The proposed method is evaluated on five datasets: Hua's, arbitrarily oriented, multi-script robust reading competition (MRRC), MSRA and video datasets with performance measures precision, recall and f-measure. The proposed method is also tested on real-time video, and the result is promising and encouraging.


Author(s):  
Zhandong Liu ◽  
Wengang Zhou ◽  
Houqiang Li

Recently, many scene text detection algorithms have achieved impressive performance by using convolutional neural networks. However, most of them do not make full use of the context among the hierarchical multi-level features to improve the performance of scene text detection. In this article, we present an efficient multi-level features enhanced cumulative framework based on instance segmentation for scene text detection. At first, we adopt a Multi-Level Features Enhanced Cumulative ( MFEC ) module to capture features of cumulative enhancement of representational ability. Then, a Multi-Level Features Fusion ( MFF ) module is designed to fully integrate both high-level and low-level MFEC features, which can adaptively encode scene text information. To verify the effectiveness of the proposed method, we perform experiments on six public datasets (namely, CTW1500, Total-text, MSRA-TD500, ICDAR2013, ICDAR2015, and MLT2017), and make comparisons with other state-of-the-art methods. Experimental results demonstrate that the proposed Multi-Level Features Enhanced Cumulative Network (MFECN) detector can well handle scene text instances with irregular shapes (i.e., curved, oriented, and horizontal) and achieves better or comparable results.


Detection and reorganization of text may save a lot of time while reproducing old books text and its chapters. This is really challenging research topic as different books may have different font types and styles. The digital books and eBooks reading habit is increasing day by day and new documents are producing every day. So in order to boost the process the text reorganization using digital image processing techniques can be used. This research work is using hybrid algorithms and morphological algorithms. For sample we have taken an letter pad where the text and images are separated using algorithms. The another objective of this research is to increase the accuracy of recognized text and produce accurate results. This research worked on two different concepts, first is concept of Pixel-level thresholding processing and another one is Otsu Method thresholding.


2016 ◽  
Vol 256 ◽  
pp. 319-327 ◽  
Author(s):  
Mario Rosso ◽  
Ildiko Peter ◽  
Ivano Gattelli

During the last decades under the enthusiastic and competent guidance of Mr Chiarmetta SSM processes attained in Italy at Stampal Spa (Torino) an unquestionable high level of industrial development with the production of large numbers of high performance automotive parts, like variety of suspension support, engine suspension mounts, steering knuckle, front suspension wheel, arm and rear axle. Among the most highlighted findings SSM processes demonstrated their capability to reduce the existing gap between casting and forging, moreover during such a processes there are the opportunity to better control the defect level.Purpose of this paper is to highlight the research work and the SSM industrial production attained and developed by Mr G.L. Chiarmetta, as well as to give an overview concerning some alternative methods for the production of enhanced performance light alloys components for critical industrial applications and to present an analysis of a new rheocasting process suitable for the manufacturing of high performance industrial components.


2021 ◽  
Vol 31 (02) ◽  
pp. 2150020
Author(s):  
Chunyan Gao ◽  
Fangqi Chen

This study develops a general model of delayed p53 regulatory network in the DNA damage response by introducing microRNA 192-mediated positive feedback loop based on the existing research work. Through theoretical analysis and numerical simulation, we find that the delay as a bifurcation parameter can drive the p53-Mdm2 module to undergo a supercritical Hopf bifurcation, thereby producing oscillation behavior. Moreover, we demonstrate how the positive feedback loop formed by p53* and microRNA 192 (miR-192) with the feature of double-negative regulation produces oscillations. Further, a comparison is given to demonstrate that microRNA 192-mediated positive feedback loop affects the robustness of system oscillations. In addition, we show that ataxia telangiectasia mutated kinase (ATM), once activated by DNA damage, makes p53* undergo two Hopf bifurcations. These results reveal that both time delay and miR-192 play tumor suppressing roles by promoting p53 oscillation or high level expression, which will provide a perspective for promoting the development of anti-cancer drugs by targeting miR-192 and time delay.


Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3099 ◽  
Author(s):  
Cheng Zhao ◽  
Li Sun ◽  
Pulak Purkait ◽  
Tom Duckett ◽  
Rustam Stolkin

In this paper, a novel Pixel-Voxel network is proposed for dense 3D semantic mapping, which can perform dense 3D mapping while simultaneously recognizing and labelling the semantic category each point in the 3D map. In our approach, we fully leverage the advantages of different modalities. That is, the PixelNet can learn the high-level contextual information from 2D RGB images, and the VoxelNet can learn 3D geometrical shapes from the 3D point cloud. Unlike the existing architecture that fuses score maps from different modalities with equal weights, we propose a softmax weighted fusion stack that adaptively learns the varying contributions of PixelNet and VoxelNet and fuses the score maps according to their respective confidence levels. Our approach achieved competitive results on both the SUN RGB-D and NYU V2 benchmarks, while the runtime of the proposed system is boosted to around 13 Hz, enabling near-real-time performance using an i7 eight-cores PC with a single Titan X GPU.


Author(s):  
V. T. Kryvosheyev ◽  
V. V. Makogon ◽  
Ye. Z. Ivanova

Economic hardship in Ukraine during the years of independence led to a sharp reduction of exploration work on oil and gas, a drop in hydrocarbon production, a decrease in inventories and a sharp collapse of research work to ensure the growth of hydrocarbon reserves.The hydrocarbon potential of various sources of Ukrainian subsoil is quite powerful and can provide future energy independence of the country. Potential hydrocarbon resources in traditional traps of various types are exhausted by only 25 %. Ukraine has recently experienced so-called “shale gas boom”. The experience of extraction of shale gas in desert areas of the United States can not be repeated in densely populated Ukraine in the absence of such powerful shale strata, resource base, necessary infrastructure, own technologies and techniques and economic, environmental and social risks.Taking into account the fuel and energy problems of the state, we constantly throughout the years of independence oriented the oil and gas industry and the authorities on the active use of our own reserves and opportunities for accelerated opening of new oil and gas fields.The results of geological exploration work in the old oil and gas basins at the high level of their study indicate that deposits in non-structural traps dominate among open deposits.A complex of sequence-stratigraphical, lithology-facies and lithology-paleogeographical studies is being successfully used to forecast undeformational traps in well-studied oil and gas bearing basin of the Ukraine – the Dniprovsko-Donetsky basin. The authors predict wide development of stratigraphic, lithologic, tectonic and combined traps in terrigenous sediments of Tournaisian and Visean age, reef-carbonate massifs of the lower Tournaisian, lower and middle Visean age and others. They should become the basis for exploration of oil and gas fields for the near and medium term and open the second breath of the basin.


Author(s):  
Rajkumar Sah ◽  
Santpal Dixit

Background: Livestock genetic diversity studies focus on their within and diversity, breed history, adaptive variations, ancestral information, site of domestication and parentage testing and assess the genetic uniformity, admixture or subdivision, inbreeding, or introgression in the population which is helpful in breed formation and their sustainable utilization.Methods: The present research work was conducted during the year 2016-17 at National Bureau of Animal Genetics Resources, Karnal-132001. STR data of 25 markers on 1237 random samples of 27 goat populations was used for analysis. The genetic diversity analysis of new population viz: Narayanpatna, Raighar, Kalahandi, Malkangiri of Odisha state and Rohilkhandi (UK) and their association studies with other Indian goat breeds was performed.Result: It was found that used markers are highly polymorphic- and the studied breeds/population showed great diversity and distributed mostly on the basis of physio-geographical condition and type of production but among new populations diversity was least which might be due to exchange of animal for breeding purposes. The studied new goat populations were well differentiated from other goat breeds which might be due to physio-geographical condition and breeding practices, so these may be considered as separate breeds/populations. In conclusion, the results showed high level of conserved genetic diversity in the Indian goat breeds. The smaller and isolated new population showed less diversity and a higher inbreeding level as compared to registered breeds.


Author(s):  
Rachna Singh ◽  
Arvind Rajawat

FPGAs have been used as a target platform because they have increasingly interesting in system design and due to the rapid technological progress ever larger devices are commercially affordable. These trends make FPGAs an alternative in application areas where extensive data processing plays an important role. Consequently, the desire emerges for early performance estimation in order to quantify the FPGA approach. A mathematical model has been presented that estimates the maximum number of LUTs consumed by the hardware synthesized for different FPGAs using LLVM.. The motivation behind this research work is to design an area modeling approach for FPGA based implementation at an early stage of design. The equation based area estimation model permits immediate and accurate estimation of resources. Two important criteria used to judge the quality of the results were estimation accuracy and runtime. Experimental results show that estimation error is in the range of 1.33% to 7.26% for Spartan 3E, 1.6% to 5.63% for Virtex-2pro and 2.3% to 6.02% for Virtex-5.


Author(s):  
A. Hergt ◽  
U. Siller

The development of modern axial compressors has already reached a high level. Therefore an enlargement of the design space by means of new or advanced aerodynamic methods is necessary in order to achieve further enhancements of performance and efficiency. The tandem arrangement of profiles in a transonic compressor blade row is such a method. For an efficient industrial application the knowledge of the fundamental design principles is needed. This paper presents the recent research work on transonic compressor tandem profiles at DLR Institute of Propulsion Technology. It deals with the fundamental description of the operation principles of a modern transonic compressor tandem cascade. By considering these principles and based on an optimization database with over 1200 members design recommendations are developed.


Sign in / Sign up

Export Citation Format

Share Document