Publikation
Scanning Neural Network for Text Line Recognition
Sheikh Faisal Rashid; Faisal Shafait; Thomas Breuel
In: IAPR International Workshop on Document Analysis Systems. IAPR International Workshop on Document Analysis Systems (DAS-12), 10th, March 27-29, Gold Coast, Queensland, Australia, IEEE, 3/2012.
Zusammenfassung
Optical character recognition (OCR) of machine
printed Latin script documents is ubiquitously claimed as a
solved problem. However, error free OCR of degraded or noisy
text is still challenging for modern OCR systems. Most recent
approaches perform segmentation based character recognition.
This is tricky because segmentation of degraded text is itself
problematic. This paper describes a segmentation free text
line recognition approach using multi layer perceptron (MLP)
and hidden markov models (HMMs). A line scanning neural
network –trained with character level contextual information
and a special garbage class– is used to extract class probabilities
at every pixel succession. The output of this scanning neural
network is decoded by HMMs to provide character level
recognition. In evaluations on a subset of UNLV-ISRI document
collection, we achieve 98.4% character recognition accuracy
that is statistically significantly better in comparison with
character recognition accuracies obtained from state-of-the-art
open source OCR systems.