Document Image Coding and Clustering for Script Discrimination
This addresses script discrimination for historical document analysis, but it is incremental as it builds on existing texture analysis and clustering techniques.
The paper tackles the problem of discriminating documents written in different scripts by mapping text to a coded 1-D image and using texture analysis for feature extraction, achieving superior performance over state-of-the-art methods on historical document databases.
The paper introduces a new method for discrimination of documents given in different scripts. The document is mapped into a uniformly coded text of numerical values. It is derived from the position of the letters in the text line, based on their typographical characteristics. Each code is considered as a gray level. Accordingly, the coded text determines a 1-D image, on which texture analysis by run-length statistics and local binary pattern is performed. It defines feature vectors representing the script content of the document. A modified clustering approach employed on document feature vector groups documents written in the same script. Experimentation performed on two custom oriented databases of historical documents in old Cyrillic, angular and round Glagolitic as well as Antiqua and Fraktur scripts demonstrates the superiority of the proposed method with respect to well-known methods in the state-of-the-art.