A handwriting recognition system for the classroom

ABSTRACT

The Xerox Ignite™ Educator Support System (henceforth referred to simply as Ignite™) is a data collection, analysis, and visualization workflow and software solution to assist K-12 educators. To illustrate, suppose a third-grade teacher wants to know how well her class has grasped a lesson on fractions. She would first scan her students' homework and/or exams into the Ignite system via a range of multifunctional input devices. Xerox Ignite™ reads, interprets, and analyzes the students' work in minutes. Then the teacher can select how to view the data by choosing from numerous reports. Examples are; an "at a glance" class summary that shows who needs extra help in what areas and who is ready to move on; a "context" report showing how each skill for each student is progressing over time; a grade-level performance report that helps third-grade teachers share best practices and cluster students into learning groups; and a student feedback report that tells each student what he/she needs to improve upon. Ignite™ intent is also to make it easier for districts to administer, score and evaluate content based on academic goals set for schools and students. The scanning and 'mark lifting' technology embedded into Ignite™ reduces the time needed to correct papers and frees time for the teacher to apply detailed insights to their day-to-day instruction tasks. Critical to this function is the automated reading of student marks, including handwriting, to enable the digitization of student performance at a detailed level. In this paper we present a system level description of the Ignite™ handwriting recognition module and describe the challenges and opportunities presented in an educational environment.

References

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Index Terms

• Applied computing

  • Education

    • Computer-assisted instruction

• Computer systems organization

  • Architectures

    • Other architectures

      • Neural networks

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Interest point and salient region detections

  • Machine learning

    • Machine learning approaches

      • Neural networks