Word separation of unconstrained handwritten text lines in PCR forms

An approach for segmenting handwritten text in a pre-hospital care report (PCR) is presented. Segmentation of lines and words in a PCR is extremely challenging due to the nature of the environment in which the reports are created, giving rise to low quality, poorly written, loosely constrained data. Stroke analyses are performed and image primitives are extracted for word detection. A heuristics-based approach, involving gap spacing, height transitions, and the average stroke width of the writer is used in detecting word boundaries. Carbon copies of live PCRs are used for testing. Experiments show perfect segmentation of 69%, outperforming the more tested and proven algorithms by as much as 15%.