Full metadata record
|dc.contributor.advisor||Fink, Gernot A.||-|
|dc.description.abstract||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.||en|
|dc.subject||Handwritten character recognition||en|
|dc.title||Lampung handwritten character recognition||en|
|Appears in Collections:||Mustererkennung in Eingebetteten Systemen|
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