In this paper, a model is proposed to learn logical structure of fixed-layout document pages by combining support vector machine (SVM) and conditional random fields (CRF). Features related to each logical label and their dependencies are extracted from various original Portable Document Format (PDF) attributes. Both local evidence and contextual dependencies are integrated in the proposed model so as to achieve better logical labeling performance. With the merits of SVM as local discriminative classifier and CRF modeling contextual correlations of adjacent fragments, it is capable of resolving the ambiguities of semantic labels. The experimental results show that CRF based models with both tree and chain graph structures outperform the SVM model with an increase of macro-averaged F1 by about 10%.
A framework is proposed in this paper to effectively and efficiently beautify handwriting by means of a novel
nonlinear and non-Gaussian Bayesian algorithm. In the proposed framework, format and size of handwriting
image are firstly normalized, and then typeface in computer system is applied to optimize vision effect of
handwriting. The Bayesian statistics is exploited to characterize the handwriting beautification process as a
Bayesian dynamic model. The model parameters to translate, rotate and scale typeface in computer system are
controlled by state equation, and the matching optimization between handwriting and transformed typeface is
employed by measurement equation. Finally, the new typeface, which is transformed from the original one
and gains the best nonlinear and non-Gaussian optimization, is the beautification result of handwriting.
Experimental results demonstrate the proposed framework provides a creative handwriting beautification
methodology to improve visual acceptance.
A visual improvement algorithm based on Monte Carlo simulation is proposed in this paper, in order to
enhance visual effects for bad handwriting. The whole improvement process is to use well designed typeface
so as to optimize bad handwriting image. In this process, a series of linear operators for image transformation
are defined for transforming typeface image to approach handwriting image. And specific parameters of linear
operators are estimated by Monte Carlo method. Visual improvement experiments illustrate that the proposed
algorithm can effectively enhance visual effect for handwriting image as well as maintain the original
handwriting features, such as tilt, stroke order and drawing direction etc. The proposed visual improvement
algorithm, in this paper, has a huge potential to be applied in tablet computer and Mobile Internet, in order to
improve user experience on handwriting.
A spatial statistic based contour feature representation is proposed to achieve extraction of local contour feature from Chinese calligraphy character, and a features fusion strategy is designed to automatically generate new hybrid character, making well use of contour feature of calligraphy and structural feature of font. The features fusion strategy employs dilation and erosion operations iteratively to inject the extracted contour feature from Chinese calligraphy into font, which are similar to “pad” and “cut” in a sculpture progress. Experimental results demonstrate that the generated new hybrid character hold both contour feature of calligraphy and structural feature of font. Especially, two kinds of Chinese calligraphy skills called “Fei Bai” and “Zhang Mo” are imitated in the hybrid character. “Fei Bai” depicts a phenomenon that part of a stroke fade out due to the fast movement of hair brush or the lack of ink, and “Zhang Mo” describes a condition that hair brush holds so much ink that strokes overlap.
To increase the flexibility and enrich the reading experience of e-book on small portable screens, a graph based method
is proposed to perform layout analysis on Portable Document Format (PDF) documents. Digital born document has its
inherent advantages like representing texts and fractional images in explicit form, which can be straightforwardly
exploited. To integrate traditional image-based document analysis and the inherent meta-data provided by PDF parser,
the page primitives including text, image and path elements are processed to produce text and non text layer for
respective analysis. Graph-based method is developed in superpixel representation level, and page text elements
corresponding to vertices are used to construct an undirected graph. Euclidean distance between adjacent vertices is
applied in a top-down manner to cut the graph tree formed by Kruskal’s algorithm. And edge orientation is then used in a
bottom-up manner to extract text lines from each sub tree. On the other hand, non-textual objects are segmented by
connected component analysis. For each segmented text and non-text composite, a 13-dimensional feature vector is
extracted for labelling purpose. The experimental results on selected pages from PDF books are presented.
Converting the PDF books to re-flowable format has recently attracted various interests in the area of e-book reading.
Robust graphic segmentation is highly desired for increasing the practicability of PDF converters. To cope with various
layouts, a multi-layer concept is introduced to segment graphic composites including photographic images, drawings
with text insets or surrounded with text elements. Both image based analysis and inherent digital born document
advantages are exploited in this multi-layer based layout analysis method. By combining low-level page elements
clustering applied on PDF documents and connected component analysis on synthetically generated PNG image
document, graphic composites can be segmented for PDF documents with complex layouts. The experimental results on
graphic composite segmentation of PDF document pages have shown satisfactory performance.
A framework is proposed in this paper to effectively generate a new hybrid character type by means of integrating local
contour feature of Chinese calligraphy with structural feature of font in computer system. To explore traditional art
manifestation of calligraphy, multi-directional spatial filter is applied for local contour feature extraction. Then the
contour of character image is divided into sub-images. The sub-images in the identical position from various characters
are estimated by Gaussian distribution. According to its probability distribution, the dilation operator and erosion
operator are designed to adjust the boundary of font image. And then new Chinese character images are generated which
possess both contour feature of artistical calligraphy and elaborate structural feature of font. Experimental results
demonstrate the new characters are visually acceptable, and the proposed framework is an effective and efficient strategy
to automatically generate the new hybrid character of calligraphy and font.