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The research project "Herbar Digital" was started in 2007 with the aim to digitize 3.5 million dried plants on paper sheets belonging to the Botanic Museum Berlin in Germany. Frequently the collector of the plant is unknown, so a procedure had to be developed in order to determine the writer of the handwriting on the sheet. In the present work the static character was transformed into a dynamic form. This was done with the model of an inert ball which was rolled along the written character. During this off-line writer recognition, different mathematical procedures were used such as the reproduction of the write line of individual characters by Legendre polynomials. When only one character was used, a recognition rate of about 40% was obtained. By combining multiple characters, the recognition rate rose considerably and reached 98.7% with 13 characters and 93 writers (chosen randomly from the international IAM-database [3]). A global statistical approach using the whole handwritten text resulted in a similar recognition rate. By combining local and global methods, a recognition rate of 99.5% was achieved.
The methods developed in the research project "Herbar Digital" are to help plant taxonomists to master the great amount of material of about 3.5 million dried plants on paper sheets belonging to the Botanic Museum Berlin in Germany. Frequently the collector of the plant is unknown. So a procedure had to be developed in order to determine the writer of the handwriting on the sheet. In the present work the static character is transformed into a dynamic form. This is done with the model of an inert ball which is rolled through the written character. During this off-line writer recognition, different mathematical procedures are used such as the reproduction of the write line of individual characters by Legendre polynomials. When only one character is used, a recognition rate of about 40% is obtained. By combining multiple characters, the recognition rate rises considerably and reaches 98.7% with 13 characters and 93 writers (chosen randomly from the international IAM-database [3]). Another approach tries to identify the writer by handwritten words. The word is cut out and transformed into a 6-dimensional time series and compared e.g. by means of DTW-methods. A global statistical approach using the whole handwritten sentences results in a similar recognition rate of more than 98%. By combining the methods, a recognition rate of 99.5% is achieved.
The objective of this student project was for the students to develop, conduct, and supervise a training course for basic work place applications (word processing and business graphics). Students were responsible for the planning, organizing and the teaching of the course. As participants, underprivileged adolescents took part in order to learn the handling of IT applications and therefore, improve their job skills and have a better chance to get into employment. Therefore the adolescents do the role of trainees at the course. Our students worked with a population that is continually overlooked by the field.
As a result, the students trained to design and implement training courses, exercised to manage projects and increased their social responsibility and awareness concerning the way of life and living conditions of other young people. The underprivileged adolescents learned to use important business applications and increased their job skills and job chances. The overall design of our concept required extensive resources to supervise and to steer the students and the adolescents. The lecturers had to teach and to counsel the students and had to be on “stand-by” just in case they were needed to solve critical situations between the two groups of young people.
