Writer Identification Using TF-IDF for Cursive Handwritten Word Recognition

In this paper, we present two text-independent writer identification methods in a closed-world context. Both methods use on-line and off-line features jointly with a classifier inspired from information retrieval methods. These methods are local, respectively based on the character and grapheme levels. This writer identification engine may be used to personalize our cursive word recognition engine [1] to the handwriting style of the writer, resulting in an adaptive cursive word recognizer. Experiments assess the effectiveness of the proposed approaches in a context of writer identification as well as integrated to our cursive word recognizer to make it adaptive.