Overwriting repetition and crossing-out detection in online handwritten text

Noise detection in online handwritten text is an important task for data acquisition. Noise occurs in online handwritten text in various ways. For example, crossing out the previously written text due to misspelling, repeated writing of the same stroke several times following a slightly different trajectory, simply writing corrections over other text are very common. Detection of these unwanted regions is a crucial pre-processing step in automatic text recognition. Currently detection and removal/correction of such regions are often done manually after collecting the data. Particularly for large databases, this can turn into a tedious and costly procedure. Consequently, in this work, we focus on noise detection for database creation. We propose to use different density-based features to distinguish between "relevant" and "unwanted" (or noisy) parts of writing. Using a 2-class HMM based classifier we get encouraging detection rate of unwanted regions from online handwritten text.