Handwritten text recognition using geometric features

An off-line method for handwritten text recognition is proposed. It is a hybrid approach using both holistic and analytical strategies. Words are divided into vertical segments and features are extracted from these segments. Features include upper stroke, lower stroke, middle loop and first character of a word. Features' fuzzy values and relative positional information form word's global representation. The matching word is found by comparing an unknown word representation with the word representations in a word dictionary. Contextual information is then used to find matching phrase from a text dictionary. The approach allows direct conversion of ASCII form of word to its holistic representation without involving training.