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Proposer
Mahmoud Mousa
Title
Handwritten Text Recognition
Goal
The goal is to implement a deep learning-based program to analyze handwritten text written in specific language.
Description
Handwritten text recognition is a challenging task due to the complexity of the script and diversity of handwritings. However, deep learning techniques have enabled significant progress recently. To develop an HTR model, researchers collect a large dataset of handwritten text images written in specific language and corresponding transcripts. You preprocess the images to improve accuracy and extract features from the images. Then you train a deep learning model like CNNs or RNNs on the features. The trained model is evaluated on a test set to measure accuracy. Once developed, the HTR model can be deployed in real-world applications like mobile apps or web services to recognize handwritten text.
Resources
Background
Url
Difficulty Level
Moderate
Ethical Approval
None
Number Of Students
0
Supervisor
Mahmoud Mousa
Keywords
handwritten text recognition, deep-learning, image recognition, cnn
Degrees
Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Bachelor of Science in Computing Science
Postgraduate Diploma in Artificial Intelligence
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