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Learning-Free Text Line Segmentation for Historical Handwritten Documents
| Content Provider | MDPI |
|---|---|
| Author | Barakat, Berat Kurar Cohen, Rafi Droby, Ahmad Rabaev, Irina El-Sana, Jihad |
| Copyright Year | 2020 |
| Description | We present a learning-free method for text line segmentation of historical handwritten document images. This method relies on automatic scale selection together with second derivative of anisotropic Gaussian filters to detect the blob lines that strike through the text lines. Detected blob lines guide an energy minimization procedure to extract the text lines. Historical handwritten documents contain noise, heterogeneous text line heights, skews and touching characters among text lines. Automatic scale selection allows for automatic adaption to the heterogeneous nature of handwritten text lines in case the character height range is correctly estimated. In the extraction phase, the method can accurately split the touching characters among the text lines. We provide results investigating various settings and compare the model with recent learning-free and learning-based methods on the cBAD competition dataset. |
| Starting Page | 8276 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10228276 |
| Journal | Applied Sciences |
| Issue Number | 22 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-11-22 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Artificial Intelligence Text Line Segmentation Text Line Detection Text Line Extraction Learning-free Historical Handwritten Documents |
| Content Type | Text |
| Resource Type | Article |
