View Proposal

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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

Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Postgraduate Diploma in Artificial Intelligence