View Proposal
-
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
-
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Postgraduate Diploma in Artificial Intelligence