This is a project aimed at recognizing Nastaliq handwritten script, a form of Persian calligraphy. The project involves detecting writing items using contours and regrouping them based on geometric proximity.
It uses a custom dataset of Nastaliq words and character combinations along with a CNN-BDLSTM hybrid model to recognize the text. GitHub: https://github.com/aidamohseni/Handwriting_Nastaliq_Recognition_BLSTM
This project aimed at recognizing Persian handwritten characters. The project employs a hybrid model integrating Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BLSTM) networks to capture both spatial and sequential features essential for accurate recognition of Persian script.
The model is evaluated using accuracy, precision, and recall metrics. The results are validated using…