Multiple choice

Summary

Multiple choice (MC), objective response or MCQ (for multiple choice question) is a form of an objective assessment in which respondents are asked to select only correct answers from the choices offered as a list. The multiple choice format is most frequently used in educational testing, in market research, and in elections, when a person chooses between multiple candidates, parties, or policies. Although E. L. Thorndike developed an early scientific approach to testing students, it was his assistant Benjamin D. Wood who developed the multiple-choice test. Multiple-choice testing increased in popularity in the mid-20th century when scanners and data-processing machines were developed to check the result. Christopher P. Sole created the first multiple-choice examination for computers on a Sharp Mz 80 computer in 1982. It was developed to aid people with dyslexia cope with agricultural subjects, as Latin plant names can be difficult to understand and write. Structure Multi

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ICFHR2014 Competition on Arabic Writer Identification Using AHTID/MW and KHATT Databases

Fouad Slimane

This paper describes the first edition of the Arabic writer identification competition using AHTID/MW and KHATT databases held in the context of the 14th International Conference on Frontiers in Handwriting Recognition (ICFHR2014). This competition has used the new freely available Arabic Handwritten Text Images Database written by Multiple Writers (AHTID/MW) and the Arabic handwritten text database called KHATT presented in ICFHR2012. We propose three tasks in this Arabic writer identification competition: the first and second are based respectively on word and text line level using the AHTID/MW database and the third one is paragraph based using the KHATT database. We received one system for the second task, three systems for the third task and none for the first task. All systems are tested in a blind manner using a set of images kept internal. A short description of the participating groups, their systems, the experimental setup, and the observed results are presented.

2014

This paper proposes a system for text-independent writer identification based on Arabic handwriting using only 21 features. Gaussian Mixture Models (GMMs) are used as the core of the system. GMMs provide a powerful representation of the distribution of features extracted using a fixed-length sliding window from the text lines and words of a writer. For each writer a GMM is built and trained using words and text lines images of that writer. At the recognition phase, the system returns log-likelihood scores. The GMM model(s) with the highest score(s) is (are) selected depending if the score is computed in Top-1 or Top-n level. Experiments using only word and text line images from the freely available Arabic Handwritten Text Images Database written by Multiple Writers (AHTID/MW) demonstrate a good performance for the Top-1, Top-2, Top-5 and Top-10 results.

2014

Free-Shape Polygonal Object Localization

Christos Marios Christoudias, Pascal Fua, Xiaolu Sun

Polygonal objects are prevalent in man-made scenes. Early approaches to detecting them relied mainly on geometry while subsequent ones also incorporated appearance-based cues. It has recently been shown that this could be done fast by searching for cycles in graphs of line-fragments, provided that the cycle scoring function can be expressed as additive terms attached to individual fragments. In this paper, we propose an approach that eliminates this restriction. Given a weighted line-fragment graph, we use its cyclomatic number to partition the graph into managebly-sized sub-graphs that preserve nodes and edges with a high weight and are most likely to contain object contours. Object contours are then detected as maximally scoring elementary circuits enumerated in each sub-graph. Our approach can be used with any cycle scoring function and multiple candidates that share line fragments can be found. This is unlike in other approaches that rely on a greedy approach to finding candidates. We demonstrate that our approach significantly outperforms the state-of-the-art for the detection of building rooftops in aerial images and polygonal object categories from ImageNet.

Springer-Verlag Berlin2014