Bag of Characters and SOM Clustering for Script Recognition and Writer Identification

In this paper, we describe a general approach for script (and language) recognition from printed documents and for writer identification in handwritten documents. The method is based on a bag of visual word strategy where the visual words correspond to characters and the clustering is obtained by means of Self Organizing Maps (SOM). Unknown pages (words in the case of script recognition) are classified comparing their vectorial representations with those of one training set using a cosine similarity. The comparison is improved using a similarity score that is obtained taking into account the SOM organization of cluster centroids. Promising results are presented for both printed documents and handwritten musical scores.