Shape drawing tests are widely used by practitioners to assess the neuropsychological conditions of patients. Most of these neuropsychological figure drawing tests comprise a set of figures drawn on a single sheet of paper which are inspected to analyze the presence or absence of certain properties and are scored accordingly. An automated scoring system for such a test requires the extraction and identification of a particular shape from the set of figures as a vital preprocessing step. This paper presents a system for effective segmentation and recognition of shapes for a well-known clinical test, the Bender Gestalt Test (BGT). The segmentation is based on connected component analysis, morphological processing and spatial clustering while the recognition is carried out using shape context matching. Experiments carried out on offline images of hand drawn samples contributed by different subjects realize promising segmentation and classification results validating the ideas put forward in this study.
In this paper we present a novel method for multilingual artificial text extraction from still images. We propose a lexicon
independent, block based technique that employs a combination of spatial transforms, texture, edge and, gradient based
operations to detect unconstrained textual regions from still images. Finally, some morphological and geometrical
constraints are applied for fine localization of textual content. The proposed method was evaluated on two standard and
three custom developed datasets comprising a wide variety of images with artificial text occurrences in five different
languages namely English, Urdu, Arabic, Chinese and Hindi.