A probabilistic framework for handwritten text line segmentation
This addresses document digitization challenges for historical archives and multilingual applications, though it appears incremental as a hybrid method.
The authors tackled handwritten text line segmentation by combining Expectation-Maximization and variational approaches in a probabilistic framework, achieving state-of-the-art performance on multiple benchmark datasets without fine-tuning.
We successfully combine Expectation-Maximization algorithm and variational approaches for parameter learning and computing inference on Markov random felds. This is a general method that can be applied to many computer vision tasks. In this paper, we apply it to handwritten text line segmentation. We conduct several experiments that demonstrate that our method deal with common issues of this task, such as complex document layout or non-latin scripts. The obtained results prove that our method achieve state-of-the-art performance on different benchmark datasets without any particular fine tuning step.