Paper PublishingHandwritten Character Recognition System
Objective
In this project we are designing a image segmentation based Handwritten character recognition system using deep neural networks.
Abstract
In this Project, we develop an innovative method for offline handwritten character detection using deep neural networks. In today’s world it has become easier to train deep neural networks because of availability of huge amount of data and various Algorithmic innovations which are taking place.
Now-a-days the amount of computational power needed to train a neural network has increased due to the availability of GPU’s and other cloud-based services like Google Cloud platform and Amazon Web Services which provide resources to train a neural network on the cloud. We have designed an image segmentation based Handwritten character recognition system. In our system we have made use of OpenCV for performing Image processing and have used TensorFlow for training the neural Network. We have developed this system using python programming language.
Keywords: Handwritten Character Recognition, Handwriting Reader, Machine Library, Handwritten Character Recognition Project, Python Handwritten Character Recognition Project.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
- Processor: I3/Intel
- Processor RAM: 4GB (min)
- Hard Disk: 128 GB
- Key Board: Standard Windows Keyboard
- Mouse: Two or Three Button Mouse
- Monitor: Any
SOFTWARE SPECIFICATIONS:
- Operating System: Windows 7+
- Server-side Script: Python 3.6+
- IDE: PyCharm
- Libraries Used: Pandas, Numpy,os,TensorFlow,opencv,Matplotlib.
Learning Outcomes
- Uses of Unsupervised Learning.
- Importance of classification.
- Scope of malware detection.
- Importance of PyCharm IDE.
- How CNN works.
- What is OpenCV?
- Difference between LSTM and RNN.
- Process of debugging a code.
- Input and Output modules
- How test the project based on user inputs and observe the output
- Project Development Skills:
- Problem analysing skills.
- Problem solving skills.
- Creativity and imaginary skills.
- Programming skills.
- Deployment.
- Testing skills.
- Debugging skills.
- Project presentation skills.
- Thesis writing skills.
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
