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

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(a) 1617: index of volume 69 (b) 1729: volume 127 (c) 1860: volume 200 Figure 2: Llibre d’esposalles (Archive of Barcelona Cathedral, ACB) segmentation step has to be done. Other good characteristics present in the documents are that the text is hardly in cursive, the documents are very clean, the words are connected and grammatical structure is similar in all the registers: day of the wedding, name and job of the husband, parents’ husband, name of the wife, parents’ wife and place where was the wedding. Figure 3: Structure of the documents: (a) husband’s surname, (b) wedding, (c) tax of the wedding Our work is part of a big project leaded by the Center for Demographic Studies (CED), Department of Geography, Universitat Autònoma de Barcelona (UAB). This project brings together researches of social sciences and computer sciences. From the perspective of scholars in social sciences, this collections is a rich source of information to construct genealogy of people over centuries. Thus, the first aim is to construct a database of marriages. It would be an artisanal and time consuming task, so word spotting techniques can help in pseudo-automatizing this process. Ground truth We have a ground truths composed by 500 documents of the volume 69 of the Cathedral of Barcelona volumes. We have also a second ground truth. It is a subset of the first ground truth. This one is composed by 30 documents, they are the same of the first ground truth. These documents have been labelled in a manual process. The labelled process has been done using an application program to label documents. This program allows us to select an area of the image and label it with a word. Each word (and the point marks of the area selected) is automatically saved in a XML file. 6
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. 7
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