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$3 USD / time
TURKEYs flagg
istanbul, turkey
$3 USD / time
Det er for øyeblikket 10:31 a.m. her
Ble med juli 11, 2017
0 Anbefalinger

Muhammad Shahzeb Khan G.

@MShahzebkhan

 5,0 (1 omtale)
1,8
1,8
100%
100%
$3 USD / time
TURKEYs flagg
istanbul, turkey
$3 USD / time
100 %
Jobber fullført
100 %
Innenfor budsjett
100 %
I tide
N/A
Gjenansettelsesrate

Technology Solution Provider

A dedicated team of researchers working in different fields such as Image Processing, Artificial Intelligence, Deep Learning, Embedded system designing, Website designing and IT solutions. Apart from the research, we are very much interested in providing the application-based solutions in all of the above mentioned fields. The basic aim is to develop good relations with the clients providing the best available solution for their problems with quality results and proper documentation.
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Portefølje

3645874
A very basic game developed for kids gaming Kiosk for a Pre-school.
Frog Crossing Game
A very basic game developed for kids gaming Kiosk for a Pre-school.
Frog Crossing Game
3645869
A window based system game developed as per client requirements i.e. the game should be similar to the very famous android game flappy bird. It was connected with the SQL database for saving player information and top scorer.
Flappy Bird windows based touch screen game
A window based system game developed as per client requirements i.e. the game should be similar to the very famous android game flappy bird. It was connected with the SQL database for saving player information and top scorer.
Flappy Bird windows based touch screen game
A window based system game developed as per client requirements i.e. the game should be similar to the very famous android game flappy bird. It was connected with the SQL database for saving player information and top scorer.
Flappy Bird windows based touch screen game
3613653
The task of this project was to perform OCR using convolution neural network on variable length captcha (max length = 7). The captcha characters were overlapping and sometimes the last letter is almost out of the picture i.e. more than half, which makes it difficult to decrypt using traditional OCR techniques. size of captcha images = 50x150x3 No of Convolution layers = 3 No of Fully connected layers = 2 Accuracy achieved on test samples = 83% (although the client required 50%) Training samples = 5000 Testing samples = 1000 The project was completed in 2 days once all the data was provided.
OCR on captcha images using Convolution neural network
The task of this project was to perform OCR using convolution neural network on variable length captcha (max length = 7). The captcha characters were overlapping and sometimes the last letter is almost out of the picture i.e. more than half, which makes it difficult to decrypt using traditional OCR techniques. size of captcha images = 50x150x3 No of Convolution layers = 3 No of Fully connected layers = 2 Accuracy achieved on test samples = 83% (although the client required 50%) Training samples = 5000 Testing samples = 1000 The project was completed in 2 days once all the data was provided.
OCR on captcha images using Convolution neural network
The task of this project was to perform OCR using convolution neural network on variable length captcha (max length = 7). The captcha characters were overlapping and sometimes the last letter is almost out of the picture i.e. more than half, which makes it difficult to decrypt using traditional OCR techniques. size of captcha images = 50x150x3 No of Convolution layers = 3 No of Fully connected layers = 2 Accuracy achieved on test samples = 83% (although the client required 50%) Training samples = 5000 Testing samples = 1000 The project was completed in 2 days once all the data was provided.
OCR on captcha images using Convolution neural network
The task of this project was to perform OCR using convolution neural network on variable length captcha (max length = 7). The captcha characters were overlapping and sometimes the last letter is almost out of the picture i.e. more than half, which makes it difficult to decrypt using traditional OCR techniques. size of captcha images = 50x150x3 No of Convolution layers = 3 No of Fully connected layers = 2 Accuracy achieved on test samples = 83% (although the client required 50%) Training samples = 5000 Testing samples = 1000 The project was completed in 2 days once all the data was provided.
OCR on captcha images using Convolution neural network
The task of this project was to perform OCR using convolution neural network on variable length captcha (max length = 7). The captcha characters were overlapping and sometimes the last letter is almost out of the picture i.e. more than half, which makes it difficult to decrypt using traditional OCR techniques. size of captcha images = 50x150x3 No of Convolution layers = 3 No of Fully connected layers = 2 Accuracy achieved on test samples = 83% (although the client required 50%) Training samples = 5000 Testing samples = 1000 The project was completed in 2 days once all the data was provided.
OCR on captcha images using Convolution neural network
3592261
Antenna pedestal unit desktop application for the tracking of drone and maintaining a line of sight. The application is communicating with the embedded systems which control the motion of AC servo motors used for elevation and azimuth.
Antenna Pedestal Unit
Antenna pedestal unit desktop application for the tracking of drone and maintaining a line of sight. The application is communicating with the embedded systems which control the motion of AC servo motors used for elevation and azimuth.
Antenna Pedestal Unit
Antenna pedestal unit desktop application for the tracking of drone and maintaining a line of sight. The application is communicating with the embedded systems which control the motion of AC servo motors used for elevation and azimuth.
Antenna Pedestal Unit
Antenna pedestal unit desktop application for the tracking of drone and maintaining a line of sight. The application is communicating with the embedded systems which control the motion of AC servo motors used for elevation and azimuth.
Antenna Pedestal Unit
3645874
A very basic game developed for kids gaming Kiosk for a Pre-school.
Frog Crossing Game
A very basic game developed for kids gaming Kiosk for a Pre-school.
Frog Crossing Game
3645869
A window based system game developed as per client requirements i.e. the game should be similar to the very famous android game flappy bird. It was connected with the SQL database for saving player information and top scorer.
Flappy Bird windows based touch screen game
A window based system game developed as per client requirements i.e. the game should be similar to the very famous android game flappy bird. It was connected with the SQL database for saving player information and top scorer.
Flappy Bird windows based touch screen game
A window based system game developed as per client requirements i.e. the game should be similar to the very famous android game flappy bird. It was connected with the SQL database for saving player information and top scorer.
Flappy Bird windows based touch screen game
3613653
The task of this project was to perform OCR using convolution neural network on variable length captcha (max length = 7). The captcha characters were overlapping and sometimes the last letter is almost out of the picture i.e. more than half, which makes it difficult to decrypt using traditional OCR techniques. size of captcha images = 50x150x3 No of Convolution layers = 3 No of Fully connected layers = 2 Accuracy achieved on test samples = 83% (although the client required 50%) Training samples = 5000 Testing samples = 1000 The project was completed in 2 days once all the data was provided.
OCR on captcha images using Convolution neural network
The task of this project was to perform OCR using convolution neural network on variable length captcha (max length = 7). The captcha characters were overlapping and sometimes the last letter is almost out of the picture i.e. more than half, which makes it difficult to decrypt using traditional OCR techniques. size of captcha images = 50x150x3 No of Convolution layers = 3 No of Fully connected layers = 2 Accuracy achieved on test samples = 83% (although the client required 50%) Training samples = 5000 Testing samples = 1000 The project was completed in 2 days once all the data was provided.
OCR on captcha images using Convolution neural network
The task of this project was to perform OCR using convolution neural network on variable length captcha (max length = 7). The captcha characters were overlapping and sometimes the last letter is almost out of the picture i.e. more than half, which makes it difficult to decrypt using traditional OCR techniques. size of captcha images = 50x150x3 No of Convolution layers = 3 No of Fully connected layers = 2 Accuracy achieved on test samples = 83% (although the client required 50%) Training samples = 5000 Testing samples = 1000 The project was completed in 2 days once all the data was provided.
OCR on captcha images using Convolution neural network
The task of this project was to perform OCR using convolution neural network on variable length captcha (max length = 7). The captcha characters were overlapping and sometimes the last letter is almost out of the picture i.e. more than half, which makes it difficult to decrypt using traditional OCR techniques. size of captcha images = 50x150x3 No of Convolution layers = 3 No of Fully connected layers = 2 Accuracy achieved on test samples = 83% (although the client required 50%) Training samples = 5000 Testing samples = 1000 The project was completed in 2 days once all the data was provided.
OCR on captcha images using Convolution neural network
The task of this project was to perform OCR using convolution neural network on variable length captcha (max length = 7). The captcha characters were overlapping and sometimes the last letter is almost out of the picture i.e. more than half, which makes it difficult to decrypt using traditional OCR techniques. size of captcha images = 50x150x3 No of Convolution layers = 3 No of Fully connected layers = 2 Accuracy achieved on test samples = 83% (although the client required 50%) Training samples = 5000 Testing samples = 1000 The project was completed in 2 days once all the data was provided.
OCR on captcha images using Convolution neural network
3592261
Antenna pedestal unit desktop application for the tracking of drone and maintaining a line of sight. The application is communicating with the embedded systems which control the motion of AC servo motors used for elevation and azimuth.
Antenna Pedestal Unit
Antenna pedestal unit desktop application for the tracking of drone and maintaining a line of sight. The application is communicating with the embedded systems which control the motion of AC servo motors used for elevation and azimuth.
Antenna Pedestal Unit
Antenna pedestal unit desktop application for the tracking of drone and maintaining a line of sight. The application is communicating with the embedded systems which control the motion of AC servo motors used for elevation and azimuth.
Antenna Pedestal Unit
Antenna pedestal unit desktop application for the tracking of drone and maintaining a line of sight. The application is communicating with the embedded systems which control the motion of AC servo motors used for elevation and azimuth.
Antenna Pedestal Unit

Anmeldelser

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Viser 1 - 1 av totalt 1 omtaler
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5,0
$82,00 USD
OCR some images
5 stars. Very satisfy!!! He is really a professional on OCR and python. Best recommended. I will hire him again. Thanks.
C Programming
C# Programming
VB.NET
OpenCV
Brukeravatar
s flagg Le Q.
@litiqi
•
7 år siden

Erfaring

Research Assistant

Medipol University Istanbul
aug. 2016 - Nå
Doing research in the field of deep learning, image processing and computer vision

Design Engineer

Genesis Solutions Pvt Ltd
jun. 2016 - jul. 2017 (1 år, 1 måned)
-Proficient in C/C++, C#, VHDL. -Working on multiple Embedded Platforms e.g. FPGA, ARM, Arduino and Microchip Controllers. -Developed cross-platform GUI controls using C# code shared between Windows and all embedded targets, in the control loading project. -Conceptualized, planned, and designed software and hardware needs for project

Kontakte Muhammad Shahzeb Khan G. angående jobben din

Logg inn for å diskutere eventuelle detaljer over chat.

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C# Programming 1
PHP
Python
.NET
Windows Desktop

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