Writer adaptation or specialization is the adjustment of handwriting recognition algorithms to a specific writer's
style of handwriting. Such adjustment yields significantly improved recognition rates over counterpart general
recognition algorithms. We present the first unconstrained off-line handwriting adaptation algorithm for Arabic
presented in the literature. We discuss an iterative bootstrapping model which adapts a writer-independent
model to a writer-dependent model using a small number of words achieving a large recognition rate increase in
the process. Furthermore, we describe a confidence weighting method which generates better results by weighting
words based on their length. We also discuss script features unique to Arabic, and how we incorporate them into
our adaptation process. Even though Arabic has many more character classes than languages such as English,
significant improvement was observed.
The testing set consisting of about 100 pages of handwritten text had an initial average overall recognition
rate of 67%. After the basic adaptation was finished, the overall recognition rate was 73.3%. As the improvement
was most marked for the longer words, and the set of confidently recognized longer words contained many fewer
false results, a second method was presented using them alone, resulting in a recognition rate of about 75%.
Initially, these words had a 69.5% recognition rate, improving to about a 92% recognition rate after adaptation.
A novel hybrid method is presented with a rate of about 77.2%.