Summary of An End-to-end, Segmentation-free, Arabic Handwritten Recognition Model on Khatt, by Sondos Aabed et al.
An End-to-End, Segmentation-Free, Arabic Handwritten Recognition Model on KHATT
by Sondos Aabed, Ahmad Khairaldin
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed deep learning model combines DCNN for feature extraction with Bidirectional Long-Short Term Memory (BLSTM) for sequence recognition and Connectionist Temporal Classification (CTC) loss function on the KHATT database. This segmentation-free approach achieves remarkable results, including an 84% character-level and 71% word-level recognition rate on the test dataset. The framework operates without segmentation at the line level, making it suitable for image-based sequence recognition applications such as digitizing, documentation, archiving, and text translation in fields like banking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This deep learning model can recognize handwritten Arabic text without needing to segment the images first. It’s very good at recognizing characters (84%) and words (71%). This is important because it could help with tasks like organizing and manipulating Arabic data. |
Keywords
» Artificial intelligence » Classification » Deep learning » Feature extraction » Loss function » Translation
