Digit Recognizer: Automated Identification of Calligraphy Digits Through Machine Learning
Objective
The objective of the Digit Recognizer: Automated Identification of Calligraphy Digits Through Machine Learning is to develop a machine learning model that can accurately identify calligraphy digits from handwritten samples. The model uses various image processing and machine learning techniques to recognize patterns and classify handwritten digits.
Abstract
In this project we present an innovative method for offline handwritten Digital detection using deep Mobile net. In today world it has become easier to train deep Mobile net 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 Mobile net 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 Mobile net on the cloud. We have designed a image segmentation based Handwritten Digital recognition system. In our system we have made use of OpenCV for performing Image processing and have used TensorFlow for training a neural Network. We have developed this system using python programming language.
KEYWORDS: Handwritten, Digital, segmentation.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
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