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.
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.