No-reference image quality assessment (NR-IQA) aims at computing an image quality score that best correlates
with either human perceived image quality or an objective quality measure, without any prior knowledge of
reference images. Although learning-based NR-IQA methods have achieved the best state-of-the-art results so
far, those methods perform well only on the datasets on which they were trained. The datasets usually contain
homogeneous documents, whereas in reality, document images come from different sources. It is unrealistic to
collect training samples of images from every possible capturing device and every document type. Hence, we
argue that a metric-based IQA method is more suitable for heterogeneous documents. We propose a NR-IQA
method with the objective quality measure of OCR accuracy. The method combines distortion-specific quality
metrics. The final quality score is calculated taking into account the proportions of, and the dependency among
different distortions. Experimental results show that the method achieves competitive results with learning-based
NR-IQA methods on standard datasets, and performs better on heterogeneous documents.
Symbol spotting is important for automatic interpretation of technical line drawings. Current spotting methods
are not reliable enough for such tasks due to low precision rates. In this paper, we combine a geometric matching-based
spotting method with an SVM classifier to improve the precision of the spotting. In symbol spotting, a
query symbol is to be located within a line drawing. Candidate matches can be found, however, the found
matches may be true or false. To distinguish a false match, an SVM classifier is used. The classifier is trained
on true and false matches of a query symbol. The matches are represented as vectors that indicate the qualities
of how well the query features are matched, those qualities are obtained via geometric matching. Using the
classification, the precision of the spotting improved from an average of 76.6% to an average of 97.2% on a
database of technical line drawings.
Symbol retrieval is important for content-based search in digital libraries and for automatic interpretation of
line drawings. In this work, we present a complete symbol retrieval system. The proposed system has an
off-line content-analysis stage, where the contents of a database of line drawings are represented as a symbol
index, which is a compact indexable representation of the database. Such representation allows efficient on-line
query retrieval. Within the retrieval system, three methods are presented. First, a feature grouping method for
identifying local regions of interest (ROIs) in the drawings. The found ROIs represent symbols' parts. Second,
a clustering method based on geometric matching, is used to cluster the similar parts from all the drawings
together. A symbol index is then constructed from the clusters' representatives. Finally, the ROIs of a query
symbol are matched to the clusters' representatives. The matching symbols' parts are retrieved from the clusters,
and spatial verification is performed on the matching parts. By using the symbol index we are able to achieve
a query look-up time that is independent of the database size, and dependent on the size of the symbol index.
The retrieval system achieves higher recall and precision than state-of-the-art methods.