Large databases of scanned documents (medical records, legal texts, historical documents) require natural language processing for retrieval and structured information extraction. Errors caused by the optical character recognition (OCR) system increase ambiguity of recognized text and decrease performance of natural language processing. The paper proposes OCR post correction system with parametrized string distance metric. The correction system learns specific error patterns from incorrect words and common sequences of correct words. A smoothing technique is proposed to assign non-zero probability to edit operations not present in the training corpus. Spelling correction accuracy is measured on database of OCR legal documents in English language. Language model and learning string metric with smoothing improves Viterbi-based search for the best sequence of corrections and increases performance of the spelling correction system.

In this paper we investigate the capacity of sound & timing information during typing of a password for the user identification and authentication task. The novelty of this paper lies in the comparison of performance between improved timing-based and audio-based keystroke dynamics analysis and the fusion for the keystroke authentication. We collected data of 50 people typing the same given password 100 times, divided into 4 sessions of 25 typings and tested how well the system could recognize the correct typist. Using fusion of timing (9.73%) and audio calibration scores (8.99%) described in the paper we achieved 4.65% EER (Equal Error Rate) for the authentication task. The results show the potential of using Audio Keystroke Dynamics information as a way to authenticate or identify users during log-on.

This work presents the development of a real-time framework for the research of multimodal feedback of robots/talking agents in the context of Human Robot Interaction (HRI) and Human Computer Interaction (HCI). For evaluating the framework, a Multimodal corpus is built (ENTERFACE_STEAD), and a study on the important multimodal features was done for building an active Robot/Agent listener of a storytelling experience with Humans. The experiments show that even when building the same reactive behavior models for Robot and Talking Agents, the interpretation and the realization of the behavior communicated is different due to the different communicative channels Robots/Agents offer be it physical but less human-like in Robots, and virtual but more expressive and human-like in Talking agents.