Titlebook: Document Analysis Systems VI; 6th International Wo Simone Marinai,Andreas R. Dengel Conference proceedings 2004 Springer-Verlag GmbH German

和蔼

0302-9743 held during September 8–10, 2004 at the University of Florence, Italy. Several papers represent the state of the art in a broad range of “traditional” topics such as layout analysis, applications to graphics recognition, and handwritten documents. Other contributions address the description of comp

跳动

https://doi.org/10.1057/9781403905390cify, in considerable detail, the essential features of document analysis systems that can assist in: (a) the creation of DL’s; (b) automatic indexing and retrieval of doc-images within DL’s; (c) the presentation of doc-images to DL users; (d) navigation within and among doc-images in DL’s; and (e) effective use of personal and interactive DL’s.

小争吵

https://doi.org/10.1007/978-3-658-12012-2ethod, the Kohonen map is trained to generate a set of test vectors that will train in a supervised manner a classical feed-forward network. The testing step consists then in classifying each pixel into one class out of four by feeding directly the feed forward network. The pixels belonging to the transparency class are then removed.

泛滥

Baek Buhm-Suk,Lisa Collins,Kim Yuri-based method is proposed for multi-component document image coding, where rectangular textual ROI’s are easily extracted using standard document image analysis techniques. Compared to multi-layer methods, the method is simpler and scalable, while preserving comparable visual quality at equivalent PSNR.

AWL

seruting

宽度

acrimony

Self-organizing Maps and Ancient Documentsethod, the Kohonen map is trained to generate a set of test vectors that will train in a supervised manner a classical feed-forward network. The testing step consists then in classifying each pixel into one class out of four by feeding directly the feed forward network. The pixels belonging to the transparency class are then removed.

Tinea-Capitis

Multi-component Document Image Coding Using Regions-of-Interest-based method is proposed for multi-component document image coding, where rectangular textual ROI’s are easily extracted using standard document image analysis techniques. Compared to multi-layer methods, the method is simpler and scalable, while preserving comparable visual quality at equivalent PSNR.

炸坏

Physical Layout Analysis of Complex Structured Arabic Documents Using Artificial Neural Nets tested on five different phases of newspaper image analysis: thread recognition, frame recognition, image text separation, text line recognition and line merging into blocks. The learning capability has been tested on line merging into blocks. Some promising experimental results are reported.