Continuous handwritten script recognition

Abstract

The transcription of written text images is one of the most challenging tasks in document analysis since it has to cope with the variability and ambiguity encountered in handwritten data. Only in a very restricted setting, as encountered in postal addresses or bank checks, transcription works well enough for commercial applications. In the case of unconstrained modern handwritten text, recent advances have pushed the field towards becoming interesting for practical applications. For historic data, however, recognition accuracies are still far too low for automatic systems. Instead, recent efforts aim at interactive solutions in which the computer merely assists an expert creating a transcription. In this chapter, an overview of the field is given and the steps along the processing chain from the text line image to the final output are explained, starting with image normalization and feature representation. Two recognition approaches, based on hidden Markov models and neural networks, are introduced in more detail. Finally, databases and software toolkits are presented, and hints to further material are provided.