Segmentation of heterogeneous document images : an ... - Tel

[21] is another texture-based method that uses a neuro-fuzzy approach. The
accuracy of the main algorithm is about 59%. However, after morphological
operations, the authors report 96% as the accuracy of the method.
Finally, we mention the method in [69] based on Markov random fields
(MRF). The authors consider that any clique is constructed from 4-connected
neighbors, and the interaction terms only consider horizontal and vertical directions.
For each node, nine features are gathered from each scale and just two
scales are considered. These features are density values of black pixels (text)
associated to the current clique in both scales. This method has not been evaluated
on popular datasets; however, we mention it because MRFs are one of the
predecessors to what we use in our method involving conditional random fields.
tel-00912566, version 1 - 2 Dec 2013
Texture-based methods on their own can be used to classify and isolate text
regions, but they are not specifically designed to separate regions of text. As a
consequence, refinement and merging operations are usually needed to correct
segmentation results. Because of these merging operations, under-segmentation
often occurs. The second issue is that in many circumstances, parts of graphical
elements are situated close to text regions, so they are recognized as text
and are merged with the text region. The third and fundamental problem with
texture-based methods is that there is no theoretical proof about which filtering
method is better than the other except to empirically report the results. The
main three parts of a texture-based method is the filtering strategy, the classifier
and the training method. So many authors publish methods by changing one
or all parts and report new results.
2.2.4 Conclusion
In conclusion, texture-based methods that are solely based on image filtering, fit
best for finding text in complex scenes other than document images. Example
of such scenes might be text detection from images of vehicle’s plates registration
or from camera-captured scenes. Methods based on whitespace analysis
and distance-based methods have their own drawbacks. Clearly, a method is
desirable that takes all consideration into account.
2.3 Text line detection
Detecting text lines in document images is a critical step towards optical character
recognition (OCR) or handwritten character recognition (HCR). This process
refers to the segmentation of each text region into distinct entities, namely
text lines. The overall performance of an OCR/HCR system strongly relies
upon the results from text line detection process. Different methods have been
proposed for detecting printed and handwritten text lines. In both cases there
exist several challenges; however, in printed documents, detecting text lines is
a rather straightforward process and is not a fit for detecting handwritten text
lines. In contrast, most of the proposed methods for detecting handwritten text
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