Optical character recognition (OCR) is a challenging task because most existing preprocessing approaches are
sensitive to writing style, writing material, noises and image resolution. Thus, a single recognition system cannot
address all factors of real document images. In this paper, we describe an approach to combine diverse recognition
systems by using iVector based features, which is a newly developed method in the field of speaker verification.
Prior to system combination, document images are preprocessed and text line images are extracted with different
approaches for each system, where iVector is transformed from a high-dimensional supervector of each text line
and is used to predict the accuracy of OCR. We merge hypotheses from multiple recognition systems according
to the overlap ratio and the predicted OCR score of text line images. We present evaluation results on an Arabic
document database where the proposed method is compared against the single best OCR system using word
error rate (WER) metric.
This paper describes a system for script identification of handwritten
word images. The system is divided into two main
phases, training and testing. The training phase performs a
moment based feature extraction on the training word images
and generates their corresponding feature vectors. The testing
phase extracts moment features from a test word image
and classifies it into one of the candidate script classes using
information from the trained feature vectors. Experiments
are reported on handwritten word images from three scripts:
Latin, Devanagari and Arabic. Three different classifiers are
evaluated over a dataset consisting of 12000 word images in
training set and 7942word images in testing set. Results show
significant strength in the approach with all the classifiers having
a consistent accuracy of over 97%.