We propose an improved HMM formulation for offline handwriting recognition (HWR). The main contribution of this
work is using modified quadratic discriminant function (MQDF) [1] within HMM framework. In an MQDF-HMM the
state observation likelihood is calculated by a weighted combination of MQDF likelihoods of individual Gaussians of
GMM (Gaussian Mixture Model). The quadratic discriminant function (QDF) of a multivariate Gaussian can be rewritten
by avoiding the inverse of covariance matrix by using the Eigen values and Eigen vectors of it. The MQDF is
derived from QDF by substituting few of badly estimated lower-most Eigen values by an appropriate constant. The
estimation errors of non-dominant Eigen vectors and Eigen values of covariance matrix for which the training data is
insufficient can be controlled by this approach. MQDF has been successfully shown to improve the character recognition
performance [1]. The usage of MQDF in HMM improves the computation, storage and modeling power of HMM when
there is limited training data. We have got encouraging results on offline handwritten character (NIST database) and
word recognition in English using MQDF HMMs.
We propose a segmentation free word spotting framework using Dynamic Background Model. The proposed
approach is an extension to our previous work where dynamic background model was introduced and integrated
with a segmentation based recognizer for keyword spotting. The dynamic background model uses the local
character matching scores and global word level hypotheses scores to separate keywords from non-keywords. We
integrate and evaluate this model on Hidden Markov Model (HMM) based segmentation free recognizer which
works at line level without any need for word segmentation. We outperform the state of the art line level word
spotting system on IAM dataset.
KEYWORDS: Image segmentation, Cameras, Magnetorheological finishing, Optical character recognition, Image processing algorithms and systems, Image processing, Lutetium, Detection and tracking algorithms, Statistical modeling, Control systems
Document binarization is one of the initial and critical steps for many document analysis systems. Nowadays,
with the success and popularity of hand-held devices, large efforts are motivated to convert documents into
digital format by using hand-held cameras. In this paper, we propose a Bayesian based maximum a posteriori
(MAP) estimation algorithm to binarize the camera-captured document images. A novel adaptive segmentation
surface estimation and normalization method is proposed as the preprocessing step in our work and followed by
a Markov Random Field based refine procedure to remove noises and smooth binarized result. Experimental
results show that our method has better performance than other algorithms on bad or uneven illumination
document images.
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