Summary of Gatedlexiconnet: a Comprehensive End-to-end Handwritten Paragraph Text Recognition System, by Lalita Kumari et al.
GatedLexiconNet: A Comprehensive End-to-End Handwritten Paragraph Text Recognition System
by Lalita Kumari, Sukhdeep Singh, Vaibhav Varish Singh Rathore, Anuj Sharma
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 A novel end-to-end paragraph recognition system is presented for handwritten text recognition (HTR) in computer vision, addressing challenges in segmenting and recognizing scanned document images. The proposed GatedLexiconNet model incorporates gated convolutional layers, attention mechanisms, and a connectionist temporal classification-based word beam search decoder to improve accuracy. By carefully applying gated convolutional layers to the existing LexiconNet architecture, the system achieves state-of-the-art performance on popular HTR datasets such as IAM, RIMES, and READ-16. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study presents an innovative approach to handwritten text recognition, using a neural network-based system that can recognize scanned document images. The system is designed to overcome common challenges in identifying text regions, analyzing layout diversity, and establishing accurate ground truth segmentation. By incorporating gated convolutional layers, attention mechanisms, and a word beam search decoder, the proposed GatedLexiconNet model achieves high recognition accuracies on popular HTR datasets. |
Keywords
» Artificial intelligence » Attention » Classification » Decoder » Neural network
