Sentence examples for Demographic classification from inspiring English sources

Table 1 Demographic, classification and treatment of all fractures.

This paper presents a recommender system for tourism based on the tastes of the users, their demographic classification and the places they have visited in former trips.

The database was mainly developed to support evaluation of writer identification [149] and writer demographic classification systems [150 152] but can also be used for handwriting recognition and similar related tasks.

In addition to the ground truth information of 3755 characters, information about the writers, their age and gender is also stored allowing evaluation of writer identification or demographic classification systems.

The first one concerns the demographic classification of family farms from a double perspective: a. one is essentially based on demographic variables: it considers just age and the size of family as relevant aspects to be analysed;   b. the other classification takes into account farm activity and the composition of family work.  .

Socioeconomic status (SES) and educational background were accounted for on the basis of the UK demographic classification scheme (National Readership Survey social grades) which classifies citizens as high SES A and B (N = 5), middle SES C1 and C2 (N = 8), and low SES D and E (N = 7).

The least explored area seems to be user demographics classification from handwriting and only a few databases contain the required ground truth (writer) information to evaluate such systems.

In addition, some databases also support evaluation of tasks like document layout analysis, word spotting, writer demographics classification, writer identification and writer verification.

In addition to the location and transcription of text, information about contributors is also stored in some cases allowing evaluation of writer recognition and writer demographics classification tasks as well.

Handwriting offers a number of interesting pattern classification problems including handwriting recognition, writer identification, signature verification, writer demographics classification and script recognition, etc. Research in these and similar related problems requires the availability of handwritten samples for validation of the developed techniques and algorithms.

Another interesting aspect in recent databases is that instead of simply keeping the identity of the writer, additional information including the age, gender and background of the writer is also stored allowing development and evaluation of automatic user demographics classification systems, a relatively less explored area in handwriting analysis.