Abstract: Abstract Word spotting on degraded and noisy historical documents can become a challenging task considering the computational time and memory usage required to scan the entire document image. This paper proposes a new effective technique for multi‐language word spotting using a two different feature extraction techniques, Histogram of Oriented Gradients (HOG) and Speeded Up Robust Features (SURF) features. First, regions of interest (ROIs) are extracted using a cross‐correlation measure, and the extracted ROIs are re‐ranked using feature ...
(read more)
Topics: 
Artificial intelligence
Natural language processing
Speech recognition