Handwritten Word Spotting in Old Manuscript Images using Shape ...

The models can then be used to retrieve unlabelled images of handwritten documents given a text
query
Handwriting recognition of large vocabularies in historical documents is still a very challenging
task. Nagy in [15] discusses the papers published in PAMI on document analysis during the last
20 years.
A word can be represented with different kind of features. A feature is a measurement about
the object to study, and allows to reduce all the characteristics of the image to a few that preserve
the main information in a more manageable size . There are three types of features: quantitative
(numeric) features, qualitative (symbolic) features and structured features. Quantitative features
can be discrete values (e.g.- weight, the number of computers) or interval values (e.g.- the duration
of an event). Qualitative features can be nominal or unordered (e.g.- colour) and ordinal (e.g.sound
intensity - “quiet” or “loud”). Structured features represent relational and/or hierarchical
attributes among a set of primitive patterns (e.g.- a parent node can be a generalization of children
labelled “cars”, “truck” and “motorbikes”) [28].
There are different ways to match words, it depends on the kind of features that are. For
example, the words can be matched directly computing the distance such as XOR, Euclidean
Distance Mapping (EDM), Sum of Square Differences (SSD), SLH, Hausdorff distance, etc. The
problem of these methods is that they are very sensitive to spatial variation.
One of the most widely used feature comparison algorithms in handwriting recognition is the
Dynamic Time Warping (DTW) [19; 9]. DTW is an algorithm for measuring similarity between
two sequences which may vary in time or speed. It has been widely used in the speech processing,
bio-informatics and also on the on-line handwriting communities to match 1-D signals. Even though
the features of the image are in general in 2-dimensions, it is possible to recast them in 1-dimension,
but it is possible to loose the association between columns features of images. DTW algorithm tries
to minimize the variations between the features vectors. In general, it is a method that allows a
computer to find a optimal match between two given sequences.
In holistic approaches the image word is not segmented into smaller parts, but are considered
as a whole shape [3]. Thus, the recognition uses to be performed by a shape matching algorithm
in terms of the features computed at some key points of interest. A comparative study between
a number of points of interest detectors is presented in [25]. For example corner can be detected
with the Harris detector [23], but a drawback of such detector is its sensitiveness to noise.
Cohesive Elastic Matching [12] is based on zoning, and it is possible to apply in all the text
image, it is not necessary to segment the words of the text. It is a good method to compare zones
of interest (ZOI). This algorithm is independent of the ZOI extraction method.
Hidden Markov Models (HMM) are used sometimes in word-spotting [1] to match words into
documents, but they are usually applied in documents with a reduced vocabulary and needs a
considerable learning stage.
4. Choosing the number of clusters
There are different methods in the literature to choose the number of clusters. They can be classified
in two big groups depending on how it is chosen the number of clusters. The first one is a manual
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