Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Rapid Generation of Pronunciation Dictionaries for new Domains and Languages

This dissertation presents innovative strategies and methods for the rapid generation of pronunciation dictionaries for new domains and languages. Depending on various conditions, solutions are proposed and developed. Starting from the straightforward scenario in which the target language is present in written form on the Internet and the mapping between speech and written language is close up to the difficult scenario in which no written form for the target language exists

Lampung handwritten character recognition

Lampung script is a local script from Lampung province Indonesia. The script is a non-cursive script which is written from left to right. It consists of 20 characters. It also has 7 unique diacritics that can be put on top, bottom, or right of the character. Considering this position, the number of diacritics augments into 12 diacritics. This research is devoted to recognize Lampung characters along with diacritics. The research aim to attract more concern on this script especially from Indonesian researchers. Beside, it is also an endeavor to preserve the script from extinction. The work of recognition is administered by multi steps processing system the so called Lampung handwritten character recognition framework. It is started by a preprocessing of a document image as an input. In the preprocessing stage, the input should be distinguished between characters and diacritics. The character is classified by a multistage scheme. The first stage is to classify 18 character classes and the second stage is to classify special characters which consist of two components. The number of classes after the second stage classification becomes 20 class. The diacritic is classified into 7 classes. These diacritics should be associated to the characters to form compound characters. The association is performed in two steps. Firstly, the diacritic detects some characters nearby. The character with closest distance to that diacritic is selected as the association. This is completed until all diacritics get their characters. Since every diacritic already has one-to-one association to a character, the pivot element is switched to a character in the second step. Each character collects all its diacritics as a composition of the compound characters. This framework has been evaluated on Lampung dataset created and annotated during this work and is hosted at the Department of Computer Science, TU Dortmund, Germany. The proposed framework achieved 80.64% recognition rate on this data

Neuroinformatics in Functional Neuroimaging

This Ph.D. thesis proposes methods for information retrieval in functional neuroimaging through automatic computerized authority identification, and searching and cleaning in a neuroscience database. Authorities are found through cocitation analysis of the citation pattern among scientific articles. Based on data from a single scientific journal it is shown that multivariate analyses are able to determine group structure that is interpretable as particular “known ” subgroups in functional neuroimaging. Methods for text analysis are suggested that use a combination of content and links, in the form of the terms in scientific documents and scientific citations, respectively. These included context sensitive author ranking and automatic labeling of axes and groups in connection with multivariate analyses of link data. Talairach foci from the BrainMap ™ database are modeled with conditional probability density models useful for exploratory functional volumes modeling. A further application is shown with conditional outlier detection where abnormal entries in the BrainMap ™ database are spotted using kernel density modeling and the redundancy between anatomical labels and spatial Talairach coordinates. This represents a combination of simple term and spatial modeling. The specific outliers that were found in the BrainMap ™ database constituted among others: Entry errors, errors in the article and unusual terminology

Simulating urban soil carbon decomposition using local weather input from a surface model

Non peer reviewe