Historical Handwritten Document Segmentation by Using a Weighted Loss
Samuele Capobianco|Leonardo Scommegna|Simone Marinai
In this work we propose one deep architecture to identify text and not-text regions in historical handwritten documents. particular adopt the U-net combination with a suitable weighted loss function order put more emphasis on most critical areas. We define map balance pixel frequency among classes guide training local prior rules. experiments evaluate performance of benchmark dataset. obtain good results using global metrics improving classification scores.
Lecture Notes in Computer Science|Florence Research (University of Florence)