An Open Source Testing Tool for Evaluating Handwriting Input Methods

This paper presents an open source tool for testing the recognition accuracy of Chinese handwriting input methods. The tool consists of two modules, namely the PC and Android mobile client. The PC client reads handwritten samples in the computer, and transfers them individually to the Android client in accordance with the socket communication protocol. After the Android client receives the data, it simulates the handwriting on screen of client device, and triggers the corresponding handwriting recognition method. The recognition accuracy is recorded by the Android client. We present the design principles and describe the implementation of the test platform. We construct several test datasets for evaluating different handwriting recognition systems, and conduct an objective and comprehensive test using six Chinese handwriting input methods with five datasets. The test results for the recognition accuracy are then compared and analyzed.Comment: 5 pages, 3 figures, 11 tables. Accepted to appear at ICDAR 201

Perceived ability and actual recognition accuracy for unfamiliar and famous faces

In forensic person recognition tasks, mistakes in the identification of unfamiliar faces occur frequently. This study explored whether these errors might arise because observers are poor at judging their ability to recognize unfamiliar faces, and also whether they might conflate the recognition of familiar and unfamiliar faces. Across two experiments, we found that observers could predict their ability to recognize famous but not unfamiliar faces. Moreover, observers seemed to partially conflate these abilities by adjusting ability judgements for famous faces after a test of unfamiliar face recognition (Experiment 1) and vice versa (Experiment 2). These findings suggest that observers have limited insight into their ability to identify unfamiliar faces. These experiments also show that judgements of recognition abilities are malleable and can generalize across different face categories