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

There is a high number of different words in the selected ground truth documents, and the
literal transcription (word by word) is an expensive process, consequently, only a few words are
labelled. The words selected are shown in figure 4. These words are selected because of their high
frequency of apparition in the selected documents.The ground truth has all the labelled words (20
classes). The subset of the ground truth is composed by the first 10 labelled words (10 classes).
(a) Barna (b) de Barna (c) en Barna (d) de (e) pages (f) reberè (g) dia
(h) dit (i) fill (j) filla (k) viuda (l) ab (m) habitant (n) donsella
(o) dilluns (p) dimarts (q) dimecres (r) dijous (s) divendres (t) dissapte (u) viudo
Figure 4: An example of each selected word
For the experiments we have employed a cross-validation technique[11]. Suppose that we have
a data set Z of size N x n, containing n-dimensional feature vectors describing N objects. We
choose an integer K (preferably a factor of N ) and randomly divide Z into K subsets of size N/K.
Then we use one subset to test the performance of D trained on the union of the remaining K -
1 subsets. This procedure is repeated K times, choosing a different part for testing each time. To
get the final result we average the K estimates. We have chosen K = 5 in our experiments.
3. Related work
Word-spotting was originally formulated to detect words in speech messages [10]. Later it was
used in text documents [20] for matching and indexing handwritten words of several documents.
in this context, it was first proposed by Manmatha [13], and later, a number of different word
matching algorithms were investigated. This technique needs word segmentation, and many word
segmentation approaches can be found in the literature. Relevant examples are: a scale-space word
segmentation process was proposed in [14] and a neural network work segmenting algorithm is also
presented in [26].
Rath [21] proposed an automatic retrieval system for historical handwritten documents using
relevance models. The method describes two statistical models for retrieval in large collections of
handwritten manuscripts given a text query. Both use a set of transcribed page images to learn a
joint probability distribution between features computed from word images and their transcriptions.
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