Handwritten Character recognition and Digit Recognition using Deep Learning
Handwritten Character and digit recognition is one of the most active areas of research in computer science and it is inherently difficult because of the high variability of writing styles. High recognition rates are achieved in character recognition and isolated word recognition, but we are still far from achieving high-performance recognition systems for unconstrained offline handwritten texts. Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. Our aim in the proposed work is to develop CNN-based handwritten character and digit recognition. Thus, CNN architecture is along with reduced operational complexity and cost is developed using Python.
PROJECT OUTPUT VIDEO:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
- Operating system : Windows 10.
- Coding Language : Python
- Web Framework : Flask