Automated optical recognition of degraded handwritten characters

1992 ◽  
Author(s):  
Emade Darwiche ◽  
Abhijit S. Pandya ◽  
Anil D. Mandalia
Keyword(s):  
Optical Recognition

CSM neural network for degraded printed character optical recognition

2014 ◽  
Vol 25 (5) ◽  
pp. 1171-1186 ◽  
Author(s):  
A. Namane ◽  
A. Guessoum ◽  
E.H. Soubari ◽  
P. Meyrueis
Keyword(s):  
Neural Network ◽  
Optical Recognition

Different strategies in optical recognition of polychromatic images

1992 ◽  
Vol 31 (14) ◽  
pp. 2560 ◽  
Author(s):  
María S. Millán ◽  
María J. Yzuel ◽  
Juan Campos ◽  
Carlos Ferreira
Keyword(s):  
Optical Recognition

ConvNet-Based Optical Recognition for Engineering Drawings

2017 ◽  
Author(s):  
Andrew Brock ◽  
Theodore Lim ◽  
J. M. Ritchie ◽  
Nick Weston
Keyword(s):  
Object Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Cross Validation ◽  
Convolutional Networks ◽  
Optical Character ◽  
Engineering Drawings ◽  
Machine Analysis ◽  
Fold Cross Validation ◽  
Optical Recognition

End-to-end machine analysis of engineering document drawings requires a reliable and precise vision frontend capable of localizing and classifying various characters in context. We develop an object detection framework, based on convolutional networks, designed specifically for optical character recognition in engineering drawings. Our approach enables classification and localization on a 10-fold cross-validation of an internal dataset for which other techniques prove unsuitable.

Azine based smart probe for optical recognition and enrichment of Mo(vi )

2018 ◽  
Vol 47 (32) ◽  
pp. 11084-11090 ◽  
Author(s):  
Milan Ghosh ◽  
Sabyasachi Ta ◽  
Jesús Sanmartín Matalobos ◽  
Debasis Das
Keyword(s):  
Aqueous Solution ◽  
Single Crystal ◽  
Green Emission ◽  
Selective Detection ◽  
X Ray ◽  
Optical Recognition

Single crystal X-ray structurally characterized azine derivative (L) was explored for selective detection of Molybdenum (Mo(vi)) cation through green emission. The L was very efficient for enrichment of Mo(vi) from aqueous solution.

Evaluation of adhd symptoms in a general sample of schoolers from 6 to 18 years old and their parents: the chip-ard study

2011 ◽  
Vol 26 (S2) ◽  
pp. 270-270
Author(s):  
H.M.O. Caci
Keyword(s):  
Rating Scales ◽  
Age And Gender ◽  
Genetic Transmission ◽  
School Aged Children ◽  
Hyperactivity Disorder ◽  
And Gender ◽  
Original Objective ◽  
Teachers And Parents ◽  
The City ◽  
Optical Recognition

IntroductionAttention-Deficit/Hyperactivity Disorder (ADHD) affects up to 5% of the school-aged children and adolescents, and to up 3% of the adults. The condition is dramatically more frequent in parents of affected children (about 30% of prevalence), therefore crediting the genetic transmission of the disorder.ObjectivesOur study was designed to increase the awareness of the disorder among teachers and parents, and to collect data for validating available rating scales for the French general population.AimsTo include up to 200 children for each year of age ranging for 5 to 18, and to collect data about their behaviour at school (rated by teachers) and at home (rated by parents). Additionally, questionnaires were filled out by the parents to rate their own symptoms. Comorbidity and related disorders were also a concern.MethodsA sample of schools was randomly selected in the city of Nice (France) and up to 4 children were randomly selected in each class. The children were included only after they and their parents gave their informed consent. Teachers rated the child behaviour on a secured website. Paper questionnaires were scanned for optical recognition.ResultsIn three months, 715 children were included and 847 parents returned their questionnaires. Preliminary analyses show that ADHD rating scales can be accurately scored by teachers and parents. References intervals need to be computed with regard of age and gender, for both children and adults.ConclusionsThe ChiP-ARD study will be reconducted in 2010-2011 to reach our original objective of 2600 children.

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