Natural Scene Character Recognition Without Dependency on
Specific Features
Muhammad Ali and Hassan Foroosh
Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida, U.S.A.
Keywords: Natural Scene Text Recognition, Tensors, Rank-1 Decomposition, Holistic Character Recognition, Feature
Independence.
Abstract: Current methods in scene character recognition heavily rely on discriminative power of local features, such
as HoG, SIFT, Shape Contexts (SC), Geometric Blur (GB), etc. One of the problems with this approach is
that the local features are rasterized in an ad hoc manner into a single vector perturbing thus spatial
correlations that carry crucial information. To eliminate this feature dependency and associated problems, we
propose a holistic solution as follows: For each character to be recognized, we stack a set of training images
to form a 3-mode tensor. Each training tensor is then decomposed into a linear superposition of ‘k’ rank-1
matrices, whereby the rank-1 matrices form a basis, spanning solution subspace of the character class. For a
test image to be classified, we obtain projections onto the pre-computed rank-1 bases of each class, and
recognize it as the class for which inner-product of mixing vectors is maximized. We use challenging natural
scene character datasets, namely Chars74K, ICDAR2003, and SVT-CHAR. We achieve results better than
several baseline methods based on local features (e.g. HoG) and show leave-random-one-out-cross validation
yield even better recognition performance, justifying thus our intuition of the importance of feature-
independency and preservation of spatial correlations in recognition.
1 INTRODUCTION
Natural scene text recognition is a challenging
problem in computer vision, machine learning and
image processing. Ubiquitous availability of digital
cameras on mobile devices e.g. phones and glasses,
has computer vision applications like assisted
navigation for visually impaired people, e.g. OrCam
1
device mounted on glasses etc. Moreover, huge
online image data repositories can be mined for
textual content to automatically generate useful
information for marketing, archival, and other
purposes. Other utilities of natural scene text
recognition include and automatic reading of
informational signs for automobile drivers or
driverless cars.
Successful commercial applications of document
text recognition ensued interest in solving the more
general problem of natural scene text recognition.
Owing to its complexity and challenges, the problem
has been broken up into the following four sub
problems:
1. Cropped Character Recognition
1
http://www.orcam.com
2. Cropped Word Recognition
3. Scene Text Detection
4. Full-image Scene Text Recognition
There is another related problem proposed by
(Wang et al., 2011) to recognize words in an image
given a short vocabulary pertaining to the image.
The introduction of ICDAR2003 reading
competition (Lucas et al., 2003) and the associated
dataset stirred up research interest in document
recognition community and subsequently other
researchers came forward and proposed their methods
and/or datasets. For example (Weinman et al., 2009)
used their sign reading dataset (WLM dataset), (de
Campos et al., 2009) proposed Chars74K, and (Wang
et al., 2011) came up with their Street View Text
(SVT) dataset. More recently (Nagy et al., 2011) put
forth NEOCR dataset.
Figure 1 shows sample images from popular
natural scene text character datasets like Chars74K
and ICDAR. The challenge is obvious from the
sample noisy images which exhibit low resolution,
variable typefaces, illumination effects, perspective
distortions, all sorts of structured and/or random
noise.
368
Ali M. and Foroosh H..
Natural Scene Character Recognition Without Dependency on Specific Features.
DOI: 10.5220/0005305603680376
In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pages 368-376
ISBN: 978-989-758-090-1
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
Figure 1: Sample characters from Chars74K (English) and
ICDAR2003 datasets.
In this paper we address the sub problem 1 above
and use ICDAR2003 robust character dataset, the
Chars74K dataset, and the SVT-CHAR, a derivative
from SVT dataset, which has been annotated by
(Mishra et al., 2012) to report our results.
The paper is organized as follows: Section 2
describes the related research work done on this
problem. Section 3 gives an overview of the tensor
rank problem. Section 4 gives describes our method
to solve the problem. Section 5 presents experimental
setup, results, and discussion. Section 6 gives a recap
of this paper along with future research directions.
2 RELATED WORK
Since the introduction of ICDAR 2003 Robust
Reading Competition and the associated challenge
datasets (Lucas et al. 2003), the area of scene text
recognition has seen an increase in research efforts to
solve the problem. Various solutions have been
proposed for the sub problem of robust character
recognition.
Some researchers used off-the-shelf OCRs to
recognize characters. (Chen and Yuille, 2004) used an
adaptive version of Niblack’s binarization algorithm
(Niblack, 1985) on the detected textual regions and
then employed commercial OCRs for final
recognition. The reported results with ABBYY
(www.abbyy.com) were good for their dataset
(collected from cameras mounted on blind people.)
Later performance of ABBYY reported by (Wang
and Belongie, 2010) and (de Campos et al., 2009)
showed its poor performance on more challenging
ICDAR and Chars74K datasets.
Overall, the literature in natural scene character
recognition is dominated by local feature-based
methods: These methods mainly focus on extracting
a feature vector, e.g. a Histogram of oriented
Gradients (HoG) (Dalal and Triggs, 2005) or some
variant of it, from a character image and then using
some classifier, e.g. Nearest Neighbor, SVM etc., to
recognize the character. (de Campos et al., 2009) used
various feature descriptors including Shape Contexts
(SC), Scale Invariant Feature Transform (SIFT),
Geometric Blur (GB), etc. in combination with bag-
of-visual-words model. The results, however, left a
lot of room for improvement. (Weinman et al., 2009)
used a probabilistic framework wherein they utilized
Gabor filters in their similarity model to recognize
characters in their dataset. (Wang and Belongie,
2010) showed better performance than (de Campos et
al., 2009) by incorporating HoG features. (Neumann
and Matas, 2011) used maximally stable extremal
regions (MSER) to create MSER mask and then got
features along its boundary which they used in SVM
for classification. (Donoser et al., 2008) used MSERs
in conjunction with simple template matching to get
initial character recognition results which are
subsequently improved by exploiting web search
engines to get final recognition results. In addition,
unsupervised feature learning system has been
proposed by (Coates et al., 2011) that utilizes a
variant of K-means clustering to first build a
dictionary then map all character images to a new
representation in the dictionary.
In this paper, we propose a novel approach for
scene character recognition based on rank-1
decomposition of image tensor. Our results show that
the proposed method effectively captures character
shape variations occurring in natural scene images in
a holistic manner thus avoiding the problems
associated with ad hoc rasterisation of local image
features.
We report our best performance on the
aforementioned popular datasets using leave-random-
one-out cross-validation, justifying thus our solution
to achieve feature-independency for better
recognition.
Figure 2: Rank-1 decomposition of a 3-mode tensor.
NaturalSceneCharacterRecognitionWithoutDependencyonSpecificFeatures
369
3 RANK-1 TENSOR
DECOMPOSITION
The rank-1 decomposition has been shown to be
effective for face recognition by (Shashua and Levin,
2001) and (Sun et al., 2011) have used it for action
recognition. In the following, we give its novel
application to characters extracted from natural scene
images.
Consider a set of character images
, where
=1,…,
and the dimensions of images be
.
Let the images be stacked together as slices of a tensor
whose elements are
,,
, where =1,…,
and
=1,…,
.
The following expresses tensor as the sum of
rank-1 tensors:
=
∑
(1)
where , are the basis vectors and
represents the
mixing coefficients. The smallest for which
equation (1) holds is called the rank of T. Figure 2
illustrates the process of rank-1 decomposition of T.
For two-image tensor, polynomial time
algorithms are available for low rank factorization.
However, when the number of slices (or images) of T
are more than ‘2’ the problem of finding such a
superposition of low rank tensors is NP-hard (Hazan
et al., 2005). Various algorithms have been proposed
to get the rank-1 factors of a multi-image (number of
images >2) tensor depending upon how the solution
space is constrained. For example, High-Order SVD
(HOSVD) (Xianqian and Sidiropoulos, 2001)
enforces orthognality constraints among the basis
vectors. (Shashua and Levin, 2001) give algorithms
to the effect of getting desirable SVD like extension
to the multi-image tensor decomposition.
We modify the greedy algorithm given in
(Shashua and Levin, 2001) to get the rank-1
decomposition of scene character image tensors. The
iterative algorithm solves the following minimization
problem to get the desired unit vectors (rank-
1elements)
and the mixing scalar vector
[λ
1
,…..,λ
p
] associated with each image.
∑‖
−
‖
(2)
where
,
,…,
is the given set of images. The
steps are summarized below:
1. Create the matrix =
∑
and find the
eigenvector corresponding to the largest eigenvalue.
This becomes the unit vector and captures the
spatial redundancy in the image set
2. Using from above, get the eigenvector
corresponding to the largest eigenvalue of the matrix
, where the columns of are
. Hence v
captures the temporal aspect of character images, e.g.
font variations etc.
3. Next, find the scalar
associated with each image
as the inner product:
4. Compute the residual image as:
=
−
, replace it with the original image
in the set and repeat the above steps until stopping
criterion is met
(Shashua and Levin, 2001) do iterative refinement
of the vectors and in step 1 and 2 respectively,
around the initially estimated location before
computing the mixing scalars. We avoid this because
we empirically found that it exacerbates noise in
scene character images and results in performance
reduction.
The stopping criteria in step 4 could be the
residual falling below a specified threshold or pre-
specifying the number of rank-1 elements. We use the
latter one and the impact of it on performance is
further discussed in (Section 5.3.2).
Figure 3: Illustration of our framework for scene character recognition.
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370
4 SCENE CHARACTER
RECOGNITION
Our framework for scene character recognition, as
depicted in Figure 3, starts with preprocessing
images, followed by tensor decomposition to extract
rank-1 basis matrices that span the training images,
projecting test images in each character class
(subspace), and finally classifying the test image
using inner product of mixing scalar vectors as a
similarity measure.
4.1 Preprocessing
The cropped character images from natural scene
datasets contain a lot of non-character structures and
imperfect cropping artefacts which makes it difficult
to effectively capture typeface and shape variations.
Since the focus of our work is to demonstrate
effectiveness of holistic recognition framework based
on rank-1 tensor decomposition, we preprocess each
image in training and testing sets to keep the images
as noise free as possible. To this end we adopt
binarization for image segmentation to reduce noise
and extract, possibly only, character structures. This
somehow lets us isolate the classification problem
from the binarization problem.
4.1.1 Image Segmentation
Binarization has been used to segment textual
information from natural scene images, e.g. see
[(Chen and Yuille, 2004), (Mishra et al. 2011), (Kita
and Wakahara, 2010), (Field and Learned-Miller,
2013)]. Binarization of natural scene character
images is a challenging problem in itself. Hence, for
the purpose of this paper, we employ a simple and
novel combination of the methods of (Yokobayashi
and Wakahara, 2005) and (Otsu, 1979) in an effort to
segment each image to get textual foreground (in
white). Using these methods, we get both the binary
image and its inverted version. For these binary
images, we then perform a connected component
analysis based on the observation that cropped
characters mostly fall in the middle of the image. We
consider any small pixel group as noise if its size is
less than a small fraction (<5%) of the size of the
largest central connected component.
2
http://www.mathworks.com.au/matlabcentral/fileexchange/
34767. Last visited: 10 December 2014.
4.1.2 Size Normalization
We then normalize each image following a convex-
hull based method given by D’Errico
2
. The intuition
is the fact that convex hull contains an edge of a
rectangle that bounds an image. We find the one that
has the least perimeter. We then try to make the image
upright by rotating the bounding rectangle so that its
major axis is vertical. This somehow corrects the slant
in the input image. Following this, we resize the
image to 32x32 pixels.
Figure 4: Preprocessing of scene character images.
4.1.3 Selection of Correct Segmentation
The four binary images from the above steps contain
a potential candidate that we select as a correct binary
image. In the case of training, we use a reference
image of a class to decide on the correct binary image.
The reference image we use is a character (for each
class) in Arial font centered in the image using
ImageMagick
3
. To get the ‘correctly’ binarized
image, we subtract each binary image from the
reference image and pick the one with the least
Frobenius norm. For testing we simply check
segmentation for all reference images and select the
one with the least Frobenius norm. Some results of
preprocessing are shown in Figure 4.
4.2 Training
For training, we stack the preprocessed images
belonging to each character class to form a mode-3
tensor. For each tensor we then apply rank-1
decomposition with a specified number of rank-1
matrices and we keep this number same across all the
classes. Hence the output of training is the specified
number of rank-1 matrices that form the subspace
basis for each class along with a set of scalar
coefficients yielding the mixing vector for each image
in that class. Figure 3 top part illustrates the training
process. The sensitivity of number of rank-1 elements
to the accuracy on test data is discussed in (Section
5.3.2).
3
http://www.imagemagick.org
NaturalSceneCharacterRecognitionWithoutDependencyonSpecificFeatures
371
4.3 Classification
To classify a test image, we first preprocess it and
then project it onto the rank-1 subspace of each
character class. The projection here means to get
inner product between the rank-1 factors and the test
image to get the mixing coefficients by the
expression:
=
; where
is the i
th
mixing
coefficient and
is the corresponding residual
image.
When =1,
= given test image. For ≥2,
=
−
. In this way, we get 62 vectors
for each test image (one per each class). We then
measure similarity of each test vector using the inner
product with the training vectors of each class and
record the maximum. The final classification is given
by taking the maximum over all classes. The process
is illustrated in the bottom part of Figure 3.
5 EXPERIMENTS
We evaluated our approach on three popular scene
character datasets Chars74K
4
, ICDAR
5
, and SVT-
CHAR
6
. We used various experimental settings to
report our results on these datasets. We also compare
our method with several baseline methods in scene
character recognition.
5.1 Datasets
The English subset of Chars74K dataset consists of
12503 characters. Characters have been cropped from
1922 images of advertisement signs and products
from stores etc. The dataset is not split in training and
testing sets, rather the authors give their proposed
training and testing splits for comparison with their
results. There is, however, a split between ‘GoodImg’
and ‘BadImg’ and as obvious from the names, the
respective splits contain ‘good’ and less noisy (7705
images) as well as ‘bad’ more noisy images (4798
images) for a total of 12503 images.
The ICDAR2003 robust character dataset
contains 11615 images of cropped scene characters
and the dataset comes split into training and testing
subsets. Characters have mostly been cropped from
images of books titles, storefronts and signs and
exhibit great variability in terms of resolution,
illumination, color, etc. The test set has 5340 images
in total but those belonging to 62 classes (A-Z, a-z,
and 0-9) are just 5379.
4
http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k
5
http://algoval.essex.ac.uk/icdar/datasets.html
The SVT-CHAR dataset consists of 3796 character
images cut out and annotated by (Mishra et al., 2012)
from cropped word images of the Street View Text
(SVT) dataset. The original SVT dataset was
harvested from Google Street View images of
businesses and storefronts by (Wang et al., 2011).
There is no training portion of SVT-CHAR and
results have been reported only using it as a test set.
5.2 Results
For all experiments, we report results using 500 rank-
1 factors for tensor decomposition. The impact of
number of rank-1 factors on accuracy is further
discussed in Section (5.3).
In our first experiment, we used the whole
ICDAR2003 training set to get the rank-1 factors for
each class of characters. The accuracy on the test set
was 69% (see Table 1). In Figure 5, the lines parallel
to the main diagonal of the confusion matrix reflect
ambiguities due to character case, e.g. small case ‘c’
confused with ‘C’, etc. (Wang et al., 2012) reported
accuracy of 83.9% on a modified version of the
ICDAR2003 test set, but they re-cropped all images
for their experiments and their set contains 5198
images, which is less than those in ICDAR2003 test
set. In Table 1, we also report our results on the
training and test splits proposed by (de Campos et al.,
2009) for Chars74K, viz., Chars74K-15, where the
suffix ‘15’ specifies the number of training and test
samples to be used for the experiment.
Table 1: Recognition performance on ICDAR2003 and
Chars74K datasets.
Method ICDAR Chars74K-15
GB+NN
(de Campos et al., 2009)
41% 47.1%
HoG+NN
(Wang and Belongie
2010)
51.5% 58%
SYNTH+FERNS
(Wang et al., 2011)
52% 47%
NATIVE+FERNS
(Wang et al., 2011)
64% 54%
MSER
(Neumann and Matas,
2011)
67% -
Proposed RANK-1
69% 57.1%
6
http://vision.ucsd.edu/~kai/svt
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372
Our second experiment was on SVT-CHAR. Since
this is just a test set, therefore, we formed its training
by combining the training sets of ICDAR2003 and the
‘GoodImg’portion of the Chars74K dataset. We
trained our factors on all 62 classes, to fairly compare
our results with the reported ones, despite the fact that
the SVT-CHAR does not contain any digit classes.
We got 64% accuracy and the results are shown in
second column of Table 2.
Figure 5: Confusion matrix for ICDAR2003 test set
Numbers 1-62 show character classes A-Z, a-z,0-9. Lines
parallel to the main diagonal show character confusions.
Table 2: Recognition performance on Chars74K-15 test
split.
Method Chars74K SVT-CHAR
ABBYY FineReader
7
31% 15.4%
GB+NN
(de Campos et al., 2009)
54.3% -
HoG+SVM (Mishra
et. al., 2012)
- 61.9%
MSER (Neumann and
Matas, 2011)
71.6% -
P
roposed RAN
K
-1
68.5% 64%
In our third experiment, we used Chars74K-15
test split while training on all Chars74K but those
images that are in the test set. Column 1 of Table 2
shows some improvement in accuracy as compared
with other baseline methods and our earlier results
given in Table 1.
The above results clearly show that our approach
does better if given more training samples which
prompted us to do another experiment with leave-
random-one-out cross-validation (CV) setting. We
show results in Table 3 for both Chars74K and
7
http://www.abbyy.com
ICDAR2003. For ICDAR2003 we combined training
and testing sets to get one big set for CV.
The results show median accuracy over 100 trials.
The accuracy of 72.5% is the best result we are aware
of on the whole Chars74K dataset (including both
GoodImg and BadImg sets). On the other hand the
results on ICDAR2003 under this setting show
improvement and further propound our observation.
Figure 6: Letters correctly recognized by our method.
Figure 7: Some of the characters that could not be correctly
recognized due to shape ambiguities, low contrast,
occlusion, imperfect cropping, large rotations etc.
Table 3: Recognition performance using leave-random-
one-out cross-validation (CV).
Method ICDAR Chars74K-15
Proposed RANK-1 + CV 76% 72.5%
Figure 6 shows some test samples from different
datasets that our approach correctly recognized.
Figure 7 shows the cases where our method failed.
Some images here are not even easy human observers
to recognize correctly due to low contrast, shape
ambiguities, noise etc.
5.3 Discussion
5.3.1 Case Sensitivity: Why It Is Important?
Cropped characters from natural scene images exhibit
extreme shape similarities for some classes. We
NaturalSceneCharacterRecognitionWithoutDependencyonSpecificFeatures
373
identified at least ten classes from English alphabet
and two digit classes 0 and 1 that can become
ambiguous in the absence of contextual clues.
Consider Figure 8. It is extremely hard even for
humans to distinguish between the pair of characters
due to case similarity. Digit ‘zero’ and ‘one’ are also
sometimes confused with letter ‘O’, ‘I’, and ‘l (ell)’
due to shape similarities. We, however, don’t take
into account digit and letter confusion and report only
letter case insensitive accuracies in Table 4.
Figure 8: Case ambiguities in natural scene characters. Top
row shows upper case while the bottom row shows lower
case letters from ICDAR2003 dataset.
Moreover, from a recognition standpoint, the case
distinction is immaterial even if eventually we are to
recognize words from characters, unless we use case
information to mark word boundaries. Table 4 shows
that we get accuracy boost over our corresponding
results in Tables 1 through 3.
Table 4: Recognition performance after removing case
sensitivity.
Method ICDAR Chars74K-15
Proposed RANK-1 80% 66%
Proposed RANK-1 +
CV
84% 78.7%
Method Chars74K SVT-CHAR
Proposed RANK-1 75.1% 73%
5.3.2 Rank-1 Elements and Number of
Samples
We give number of rank-1 elements as input to the
decomposition algorithm. Figure 9 shows the effect
of the choice of rank-1 elements on accuracy for
ICDAR test set and Chars74K set when tested on the
proposed test split by (de Campos et al., 2009). As
noted by (Shashua and Levin, 2001), addition of rank-
1 elements helps capture temporal redundancies in the
input image set that in our case occur due to font and
shape changes. This eventually gets to increased
accuracy. However, as shown in Figure 9, a point
comes after which we get a kind of stagnation in
accuracy. We, therefore, empirically fixed the
number of rank-1 elements to ‘500’.
As mentioned (in Section 5.2) above, the number
of training samples per class also played an important
role in boosting accuracy on the test set. We
empirically observed that unless we add good (or less
noisy) images to the training set, the decomposition
process would be affected by the presence of even a
small number of noisy images. This can be explained
by the fact that as the number of less noisy images
increases, the additive effect of noisy images is
reduced and a good pattern of variation in character’s
font and shape becomes available and is effectively
captured in the spatial and temporal components of
the rank-1 decomposition. This is the reason why we
used just the ‘GoodImg’ part of Chars74K when we
combined ICDAR2003 and Chars74K to train for
SVT-CHAR. To further demonstrate this fact, we plot
in Figure 10 the accuracy gain with increasing
number of training images for the character class ‘A’
Figure 9: Number of Rank-1 Elements vs. Accuracy on
ICDAR (shown in red) and Chars74K (shown in blue).
(this trend is also true for all other character classes).
The accuracy here represents performance measured
on the test samples of ‘A’ from (de Campos et al.,
2009) test split after training over different number of
available training samples of character ‘A’ from
Chars74K. We varied the sample count from ‘15’
(given in de Campos et al. training split) to ‘659’ (all
samples of ‘A’ excluding the ‘15’ given in the test
split). The plot validates our observation about gain
in accuracy with the increase in number of samples
for natural scene character recognition.
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374
6 CONCLUSIONS
We proposed a holistic approach to solve natural
scene character recognition that avoids dependency
on specific features. Our method is based on multi-
image tensor decomposition similar to (Shashua and
Levin, 2001) with modification as to the way we get
rank-1 matrices for natural scene images that contain
a lot of variations and noise. Through our results we
showed the potential of using image tensor
decomposition to better capture shape and font
variations in scene character images. We got better
results than several baseline methods and achieved
improved recognition performance on the datasets
using leave-random-one-out cross-validation,
justifying thus our intuition of the importance of
feature-independency and preservation of spatial
correlations in recognition.
In future we hope to get state-of-the-art
performance using better image segmentation
methods and also plan to incorporate recent advances
in tensor decomposition domain in solving other sub
problems of scene text recognition.
Figure 10: Accuracy vs. Number of training samples of ‘A’
from Chars74K dataset.
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