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

currently working on some of these ideas to improve our results and we decided
to set aside some others due to limitation in time.
For text and graphics separation, it would be a benefit to use three labels
of ”text”,”graphics” and ”text containers” instead of just two labels of ”text”
and ”non-text” components. Text containers refer to tables and frames that
contain text. Currently we are doing that by using a threshold on the number
of children and the solidity feature of non-textual components. Learning this
using a training dataset will make it more robust to errors but it requires a
modification to the ground truth data that we use.
tel-00912566, version 1 - 2 Dec 2013
For text region detection there is more room for improvement. Currently we
are computing several observations with Gabor features with different size of the
window and we send these observations to our feature functions. However, to
make it more robust and multi-scale what we should do is to change the window
size of the Gabor filter locally according to the local height of text connected
components. To do this, we have to compute a complete set of Gabor filters
and to convolve them with the original image to generate many filtering results.
Then for each site, based on the average local height of text components, we
have to pick the value from the filleting result that correspond to that local
height. Clearly, this approach is unfeasible due to its computation burden. It
would be desirable if one could benefit from a formulation and implementation
of non-stationary Gabor filters in which the size of the kernel changes locally
according to the local height of text components.
Feature functions in our CRF model depend on several observation maps
from the image. However, the number of features are not enough to separate
sites with great confidence. Also the dependency of these observation are not
exploited in our framework. As a result the contribution edge potentials in our
CRF model is very limited due to small number of feature functions. One can
benefit from more effective features such as Ferns [74, 73] to define feature functions
in CRF model.
As for inference in conditional random fields, we are using ICM [9] due to
its simplicity and fast training time which is less than 4 to 6 hours. However, it
is known that ICM fails to capture long-range interaction between sites’ labels.
We evaluated loopy belief propagation [64] in this work and it could not converge
to a good solution after considerable amount of time. One may benefit
from other inference methods to improve results for the CRF mdoel.
Finally, in the method for text line detection, the global median of height of
text connected components are used inside the transition and emission probabilities
of HMM. One can reformulate these probabilities to use the local average
height of text components instead of the global statistics.
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