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In the current literature about textual element identification in bank cheque images, many strategies put forward are strongly dependent on document layout. This means searching and employing contextual information as a pointer to a search region on the image. However human handwriting, as well as machine printed characters, are not dependent on the document in which they are inserted. Components of handwritten and machine printed behavior can be maintained in a generic and independent way. Based on these observations this paper presents a new approach to identifying textual elements from a set of local features enabling the category of a textual element to be established, without needing to observe its environment. The use of local features might allow a more generic and reach classificatory process, enabling it in some cases to be used over different sorts of documents. Based on this assumption, in our tests we used bank cheque images from Brazil, USA, Canada and France. The preliminary results show the efficiency and the potential of this approach.

Distinguishing between Handwritten and Machine Printed Text in Bank Cheque Images