Binarization of degraded document images based on hierarchical deep supervised network
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The document presents a hierarchical deep supervised network (DSN) for binarizing degraded document images. DSN extends convolutional neural networks to extract hierarchical features and classify pixels as text or background. It achieves state-of-the-art performance on several document image binarization datasets without pre- or post-processing. Subsequent research has adapted DSN to other document types like music scores and paychecks, and worked to reduce its computational complexity.
Binarization of degraded document images based on hierarchical deep supervised network
- 1. Binarization of Degraded Document Images Based on Hierarchical Deep Supervised Network Quang Nhat Vo, Soo Hyung Kim, Hyung Jeong Yang, and Gueesang Lee Pattern Recognition 74 (2018) 568–586 Presented by: Tarik Reza Toha #1017052013
- 2. • Problem definition – What is the research problem? • Motivation – Why have the authors done the research? • Solution approach – How have the authors solved the problem? – Be detail on this. • Subsequent advancements – What are the subsequent research studies and how have they further advanced the solution of the problem? 2 Outline
- 3. 3 Digital Archiving • Historical documents represent valuable cultural heritages that need to be protected and preserved • Automatic analysis of historical-document images involves: – Layout analysis – Text-line and word segmentation – Optical character recognition (OCR)
- 4. • Binary image representation is preferred for document analysis – Each pixel is labeled as “text” (1) or “background” (0) • Binarization of degraded document images is complicated – Non-uniform intensity – Complex background – Bleed through • Existing solutions use unsupervised approaches and low- level features – Difficult to differentiate the text from the non-text components 4 Binary Degraded Document Images
- 5. • Global binarization algorithms – Extracted labeling information is applied to the entire document images • Otsu et al., compute a threshold – minimize the within-class variance – maximize the between-class variance • Clustering-based approaches separate the text through learning of the unsupervised models • Work well with simple backgrounds and a uniform intensity 5 Existing Binarization Methods It cannot be used for degraded documents
- 6. • Local binarization algorithms – Predict based on its neighborhood information • Image binarization is a classification problem – Unsupervised-classification algorithms – Supervised learning-based approaches • parameter-free nature • no need for pre- or post-processing – Deep neural network-based approaches 6 Existing Binarization Methods (contd.)
- 7. 7 Existing Binarization Methods (contd.) Still noises and disconnected strokes exist Howe’s method vs Vo’s method on DIBCO 2011 dataset
- 8. • Hierarchical deep supervised network (DSN) – Learns different feature levels from image data itself to classify foreground and background from degraded document images • DSN extends traditional convolutional neural network (CNN) to extract different feature levels 8 Main Contribution
- 9. 9 Proposed Architecture Demonstration of the DSN model for dense prediction
- 10. 10 Proposed Architecture (contd.) Diagram of the proposed DSN-based document binarization model
- 13. 13 Experimental Evaluation Samples of the generated training image patches and ground- truth binary maps
- 16. 16 DIBCO 2013 Dataset (contd.)
- 20. 20 Other Types of Documents Korean historical document image Chinese historical document image
- 22. • The binarization of degraded document images is a challenging problem in terms of document analysis – DSN is a hierarchical architecture of deep supervised network that incorporates side layers to improve the training convergence • Future work – Handle the weak information – Adaptation to music score and paycheck – Reduce the number of convolutional layers 22 Conclusion
- 23. Thank you Questions are welcome! 23