CHARACTER RECOGNITION IN NATURAL IMAGES

Te´oﬁlo E. de Campos

Xerox Research Centre Europe, 6 chemin de Maupertuis, 38240 Meylan, France

Bodla Rakesh Babu

International Institute of Information Technology, Gachibowli, Hyderabad 500 032 India

Manik Varma

Microsoft Research India, “Scientia” 196/36 2nd Main, Sadashivnagar, Bangalore 560 080 India

Keywords:

Object recognition, Camera-based character recognition, Latin characters, Digits, Kannada characters, Off-

line handwritten character recognition.

Abstract:

This paper tackles the problem of recognizing characters in images of natural scenes. In particular, we focus

on recognizing characters in situations that would traditionally not be handled well by OCR techniques. We

present an annotated database of images containing English and Kannada characters. The database comprises

of images of street scenes taken in Bangalore, India using a standard camera. The problem is addressed in an

object cateogorization framework based on a bag-of-visual-words representation. We assess the performance

of various features based on nearest neighbour and SVM classiﬁcation. It is demonstrated that the performance

of the proposed method, using as few as 15 training images, can be far superior to that of commercial OCR

systems. Furthermore, the method can beneﬁt from synthetically generated training data obviating the need

for expensive data collection and annotation.

1 INTRODUCTION

This paper presents work towards automatic reading

of text in natural scenes. In particular, our focus is

on the recognition of individual characters in such

scenes. Figures 1, 2 and 3 highlight why this can

be a hard task. Even if the problems of clutter and

text segmentation were to be ignored for the moment,

the following sources of variability still need to be ac-

counted for: (a) font style and thickness; (b) back-

ground as well as foreground color and texture; (c)

camera position which can introduce geometric dis-

tortions; (d) illumination and (e) image resolution.

All these factors combine to give the problem a ﬂa-

vor of object recognition rather than optical character

recognition or handwriting recognition. In fact, OCR

techniques can not be applied out of the box precisely

due to these factors. Furthermore, viable OCR sys-

tems have been developed for only a few languages

and most Indic languages are still beyond the pale of

current OCR techniques.

Many problems need to be solved in order to

read text in natural images including text localization,

character and word segmentation, recognition, inte-

gration of language models and context, etc. Our fo-

cus, in this paper, is on the basic character recogni-

tion aspect of the problem (see Figures 2, 3, 5 and 6).

We introduce a database of images containing English

and Kannada text

. In order to assess the feasibility of

posing the problem as an object recognition task, we

benchmark the performance of various features based

on a bag-of-visual-words representation. The results

indicate that even the isolated character recognition

task is challenging. The number of classes can be

moderate (62 for English) to large (657 for Kannada)

with very little inter-class variation as highlighted by

Figures 2 and 3. This problem is particularly acute

for Kannada where two characters in the alphabet can

differ just by the placement of a single dot like struc-

ture. Furthermore, while training data is readily avail-

able for some characters others might occur very in-

Available at http://research.microsoft.com/˜manik/

273

E. de Campos T., Rakesh Babu B. and Varma M.

CHARACTER RECOGNITION IN NATURAL IMAGES.

DOI: 10.5220/0001770102730280

In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications (VISIGRAPP 2009), page

ISBN: 978-989-8111-69-2

Figure 1: Sample source images in our data set.

frequently in natural scenes. We therefore investigate

whether surrogate training data, either in the form of

font generated characters or hand-printed characters,

can be used to bolster recognition in such a scenario.

We also present baseline recognition results on the

font and hand-printed character databases to contrast

the difference in performance when reading text in

natural images.

2 RELATED WORK

The task of character recognition in natural scenes

is related to problems considered in camera-based

document analysis and recognition. Most of the work

in this ﬁeld is based on locating and rectifying the

text areas (e.g. (Kumar et al., 2007), (Krempp et al.,

2002), (Clark and Mirmehdi, 2002) and (Brown et al.,

2007)), followed by the application of OCR tech-

niques (Kise and Doermann, 2007). Such approaches

are therefore limited to scenarios where OCR works

well. Furthermore, even the rectiﬁcation step is not

directly applicable to our problem, as it is based on

zero O o O D K R K

4 A X A D D I l one l one 7 L

8 3 B B i 8 i H H H A

O Q T T u N Z 2

Figure 2: Examples of high visual similarity between sam-

ples of different classes caused mainly by the lack of visual

context.

Figure 3: A small set of Kannada characters, all from differ-

ent classes. Note that vowels often change a small portion

of the characters, or add disconnected components to the

character.

the detection of printed document edges or assumes

that the image is dominated by text.

Methods for off-line recognition of hand-printed

characters (Plamondon and Srihari, 2000), (Pal et al.,

2007) have successfully tackled the problem of intra-

class variation due to differing writing styles. How-

ever, such approaches typically consider only a lim-

ited number of appearance classes, not dealing with

variations in foreground/background color and tex-

ture.

For natural scenes, some researchers have de-

signed systems that integrate text detection, segmen-

tation and recognition in a single framework to ac-

commodate contextual relationships. For instance,

(Tu et al., 2005) used insights from natural language

processing and present a Markov chain framework

for parsing images. (Jin and Geman, 2006) intro-

duced composition machines for constructing prob-

abilistic hierarchical image models which accom-

modate contextual relationships. This approach al-

lows re-usability of parts among multiple entities and

non-Markovian distributions. (Weinman and Learned

Miller, 2006) proposed a method that fuses image fea-

tures and language information (such as bi-grams and

letter case) in a single model and integrates dissimi-

larity information between character images.

Simpler recognition pipelines based on classify-

VISAPP 2009 - International Conference on Computer Vision Theory and Applications

274

ing raw images have been widely explored for digits

recognition (see (le Cun et al., 1998), (Zhang et al.,

2006) and other works on the MNIST and USPS

datasets). Another approach is based on modeling this

as a shape matching problem (e.g. (Belongie et al.,

2002)): several shape descriptors are detected and ex-

tracted and point-by-point matching is computed be-

tween pairs of images.

3 DATA SETS

Our focus is on recognizing characters in images of

natural scenes. Towards this end, we compiled a

database of English and Kannada characters taken

from images of street scenes in Bangalore, India.

However, gathering and annotating a large number

of images for training can be expensive and time

consuming. Therefore, in order to provide comple-

mentary training data, we also acquired a database

of hand-printed characters and another of characters

generated by computer fonts.

For English, we treat upper and lower case char-

acters separately and include digits to get a total of 62

classes. Kannada does not differentiate between up-

per and lower case characters. It has 49 basic charac-

ters in its alpha-syllabary, but consonants and vowels

can combine to give more than 600 visually distinct

classes.

3.1 Natural Images Data Set

We photographeda set of 1922 images, mostly of sign

boards, hoardings and advertisements but we also in-

cluded a few images of products in supermarkets and

shops. Some of these original images are shown in

Figure 1.

Individual characters were manually segmented

from these images. We experimented with two types

of segmentations: rectangular bounding boxes and

ﬁner polygonal segments as shown in Figure 4. For

the types of features investigated in this paper, it

turned out that polygonal segmentation masks pre-

sented almost no advantage over bounding boxes.

Therefore, all the results presented in Section 5 are

using the bounding box segmentations.

Our English dataset has 12503 characters, of

which 4798 were labeled as bad images due to ex-

cessive occlusion, low resolution or noise. For our

experiments, we used the remaining 7705 character

images. Similarly, for Kannada, a total of 4194 char-

acters were extracted out of which only 3345 were

used. Figures 5 and 6 show examples of the extracted

Figure 4: Sample characters and their segmentation masks.

Figure 5: Sample characters of the English Img data set.

characters. These datasets will be referred to as the

Img datasets.

3.2 Font and Hand-printed Datasets

The hand-printed data set (Hnd) was captured using

a tablet PC with the pen thickness set to match the

average thickness found in hand painted information

boards. For English, a total of 3410 characters were

generated by 55 volunteers. For Kannada, a total of

16425 characters were generated by 25 volunteers.

Some sample images are shown in Figure 7.

The font dataset was synthesized only for English

characters. We tried 254 different fonts in 4 styles

(normal, bold, italic and bold+italic) to generate a to-

tal of 62992 characters. This dataset will be referred

to as the Fnt dataset.

4 FEATURE EXTRACTION AND

REPRESENTATION

Bag-of-visual-words is a popular technique for rep-

resenting image content for object category recogni-

tion. The idea is to represent objects as histograms of

CHARACTER RECOGNITION IN NATURAL IMAGES

275

Figure 6: Sample characters of the Kannada Img data set.

Figure 7: Sample hand-printed characters of the English

and Kannada data sets.

feature counts. This representation quantizes the con-

tinuous high-dimensional space of image features to

a manageable vocabulary of “visual words”. This is

achieved, for instance, by grouping the low-level fea-

tures collected from an image corpus into a speciﬁed

number of clusters using an unsupervised algorithm

such as K-Means (for other methods of generating the

vocabulary see (Jurie and Triggs, 2005)). One can

then map each feature extracted from an image onto

its closest visual word and represent the image by a

histogram over the vocabulary of visual words.

We learn a set of visual words per class and aggre-

gate them across classes to form the vocabulary. In

our experiments, we learned 5 visual words per class

for English leading to a vocabulary of size 310. For

Kannada, we learn 3 words per class, resulting in a

vocabulary of 1971 words.

4.1 Features

We evaluated six different types of local features. Not

only did we try out shape and edge based features,

such as Shape Context, Geometric Blur and SIFT, but

also features used for representing texture, such as ﬁl-

ter responses, patches and Spin Images, since these

were found to work well in (Weinman and Learned

Miller, 2006). We explored the most commonly used

parameters and feature detection methods employed

for each descriptor, with a little tuning, as described

below.

Shape Contexts (SC) (Belongie et al., 2002) is a

descriptor for point sets and binary images. We sam-

ple points using the Sobel edge detector. The descrip-

tor is a log-polar histogram, which gives a θ × n vec-

tor, where θ is the angular resolution and n is the ra-

dial resolution. We used θ = 15 and r = 4.

Geometric Blur (GB) (Berg et al., 2005) is a fea-

ture extractor with a sampling method similar to that

of SC, but instead of histogramming points, the re-

gion around an interest point is blurred according to

the distance from this point. For each region, the edge

orientations are counted with a different blur factor.

This soothes the problem of hard quantization and al-

lows its application to gray scale images.

Scale Invariant Feature Transform (SIFT)

(Lowe, 1999) are extracted on points located by the

Harris Hessian-Laplace detector, which gives afﬁne

transform parameters. The feature descriptor is com-

puted as a set of orientation histograms on (4 × 4)

pixel neighborhoods. The orientation histograms are

relative to the key-point orientation. The histograms

contain 8 bins each, and each descriptor contains a

4 × 4 array of 16 histograms around the key-point.

This leads to feature vector with 128 elements.

Spin image (Lazebnik et al., 2005), (Johnson and

Herbert, 1999) is a two-dimensional histogram encod-

ing the distribution of image brightness values in the

neighborhood of a particular reference point. The two

dimensions of the histogram are d, distance from the

center point, and i, the intensity value. We used 11

bins for distance and 5 for intensity value, resulting in

55-dimensional descriptors. The same interest point

locations used for SIFT were used for spin images.

Maximum Response of ﬁlters (MR8) (Varma

and Zisserman, 2002) is a texture descriptor based on

a set of 38 ﬁlters but only 8 responses. This ﬁlter is

extracted densely, giving a large set of 8D vectors.

Patch descriptor (PCH) (Varma and Zisserman,

2003) is the simplest dense feature extraction method.

For each position, the raw n × n pixel values are vec-

torized, generating an n

descriptor. We used 5 × 5

patches.

5 EXPERIMENTS AND RESULTS

This section describes baseline experiments using

three classiﬁcation schemes: (a) nearest neighbor

(NN) classiﬁcation using χ

statistic as a similarity

measure; (b) support vector machines (SVM); and

show results obtained by the commercial OCR system

VISAPP 2009 - International Conference on Computer Vision Theory and Applications

276

ABBYY FineReader 8.0

. For an additional bench-

mark, we provide results obtained with the dataset of

the ICDAR Robust Reading competition 2003

. This

set contains 11615 images of characters used in En-

glish. The images are more challenging than our En-

glish Img dataset and it has some limitations, such as

the fact that only a few samples are available for some

of the characters.

Most of our experiments were done on our English

Img characters dataset. It is demonstrated that the per-

formance of MKL using only 15 training images is

nearly 25% better than that of ABBYY FineReader, a

commercial OCR system. Also, when classifying the

Img test set, if appropriate features such as Geomet-

ric Blur, are used, then a NN classiﬁer trained on the

synthetic Fnt dataset is as good as the NN classiﬁer

trained on an equal number of Img samples. Further-

more, since synthetic Fnt data is easy to generate, an

NN classiﬁer trained on a large Fnt training set can

perform nearly as well as MKL trained on 15 Img

samples per class. This opens up the possibility of

improving classiﬁcation accuracy without having to

acquire expensive Img training data.

5.1 English Data Sets

5.1.1 Homogeneous Sets

This subsection shows results obtained by training

and testing on samples from the same type – i.e. Fnt,

Hnd and Img data. While our focus is on the Img

dataset, training and testing on Fnt or Hnd provide

useful baselines for comparison. For some classes,

the number of available Img samples was just above

30, so we chose to keep the experiment sets balanced.

The test set size was ﬁxed to 15 samples per class for

each of the three databases. The number of training

samples per class was varied between 5 and 15 for the

Img dataset. For Fnt and Hnd, we used 1001 and 40

samples per class respectively for training. Multiple

training and testing splits were generated at random

and the results averaged. Table 1 shows the results

obtained with training sets of 15 samples per class.

The performance of GB and SC is signiﬁcantly better

than all the other features. Also, there can be more

than a 20% drop in performance when moving from

training and testing on Fnt or Hnd to training and test-

ing on Img. This indicates how much more difﬁcult

recognizing characters in natural images can be.

The features were also evaluated using SVMs with

RBF kernels for the Img dataset, leading to the re-

sults shown in table 2. An additional experiment was

http://www.abbyy.com

http://algoval.essex.ac.uk/icdar

Table 1: Nearest neighbor classiﬁcation results (%) ob-

tained by different features on the English data sets. These

were obtained with 15 training and 15 testing samples per

class. For comparison, the results with the commercial soft-

ware ABBYY are also shown. The bottom row indicates

how many sets of training samples were taken per class to

estimate mean and standard deviation of the classiﬁcation

results.

Feature Fonts Hand Images

GB 69.71± 0.64 65.40± 0.58 47.09

SC 64.83± 0.60 67.57± 1.40 34.41

SIFT 46.94± 0.71 44.16± 0.79 20.75

Patches 44.93± 0.65 69.41± 0.72 21.40

SPIN 28.75± 0.76 26.32± 0.42 11.83

MR8 30.71± 0.67 25.33± 0.63 10.43

ABBYY 66.05± 0.00 – 30.77

# train splits 10 5 1

Table 2: Classiﬁcation results (%) obtained with 1-vs-All

SVM and with MKL (combining all the features) for the

Img set with 15 training samples per class.

GB 52.58

SC 35.48

SIFT 21.40

Patches 21.29

SPIN 13.66

MR8 11.18

MKL 55.26

performed with the multiple kernel learning (MKL)

method of (Varma and Ray, 2007), which gave state-

of-the-art results in the Caltech256 challenge. This

resulted in an accuracy of 55.26% using 15 training

samples per class.

As can be seen from these experiments, it is pos-

sible to surpass the performance of ABBYY, a state-

of-the-art commercial OCR system, using 15 train-

ing images even on the synthetic Fnt dataset. For

the more difﬁcult Img dataset the difference in per-

formance between MKL and ABBYY is nearly 25%

indicating that OCR is not suitable for this task. Nev-

ertheless, given that the performance using MKL is

only 55%, there is still tremendous scope for improve-

ment in the object recognition framework.

We also performed experiments with the ICDAR

dataset, obtaining the results in Table 3. Due to the

limitations of this dataset, we ﬁxed the training set

size to 5 samples per class and evaluated it in com-

parison to our dataset. As can be seen, the ICDAR

results are worse than the Img results indicating that

this might be an even tougher database. If we train on

Img and test on ICDAR then the results can improve

as more training data is added (see Table 4).

CHARACTER RECOGNITION IN NATURAL IMAGES

277

Table 3: Nearest neighbor results obtained with 5 training

samples per class for some of the features. Here we com-

pare our English Img dataset and with the ICDAR dataset.

Feature Img ICDAR

GB 36.9± 1.0 27.81

SC 26.1± 1.6 18.32

PCH 13.7± 1.4 9.67

MR8 6.9± 0.7 5.48

Table 4: Nearest neighbor results obtained by training with

English Img and testing with the ICDAR dataset – using 15

training samples per class and using the whole Img set for

training.

Tr. Spls. 15/class all

GB 32.72 40.97

SC 27.90 34.51

5.1.2 Hybrid Sets

In this subsection we show experiments with hybrid

sets, where we train on data from the Fnt and Hnd

datasets and test on the same 15 images per class from

the Img test set used in the previous experiments. The

results are shown in Table 5 and indicate that for fea-

tures such as Geometric Blur, training on easily avail-

able synthetic fonts is as good as training on origi-

nal Img data. However, the performance obtained by

training on Hnd is poor.

Table 5: Nearest neighbor results with mixed data: testing

the recognition of natural images using training data from

fonts and hand-printed sets, both with 15 training samples

per class. These results should be compared with the Img

column of Table 1.

Feature Training on Fnt Training on Hnd

GB 47.16± 0.82 22.95± 0.64

SC 32.39± 1.39 26.82± 1.67

SIFT 9.86± 0.91 4.02± 0.52

Patches 5.65± 0.69 1.83± 0.44

SPIN 2.88± 0.68 2.71± 0.33

MR8 1.87± 0.60 1.61± 0.11

# test splits 10 5

To aid visualization of the results, Figure 8 shows

results of the experiments described above, separat-

ing panels for the top three methods: Geometric Blur,

Shape Contexts and Patches. There is one curve

for each type of experiment, where FntImg indicates

training with Fnt and testing with Img, and HndImg

indicates training with Hnd and testing with Img.

The other curves show results by training and testing

with the same kind of set (Fnt, Hnd and Img). Note

that, for Geometric Blur, the NN performance when

trained on Fnt and tested on Img is actually better than

NN performance when trained and tested on Img.

5 7 9 11 13 15

Training set size / class

Classification accuracy

Geometric Blur

Hnd NN

Fnt NN

Img SVM

Img NN

FntImg NN

HndImg NN

5 7 9 11 13 15

Training set size / class

Classification accuracy

Hnd NN

Fnt NN

Img NN

FntImg NN

HndImg NN

5 7 9 11 13 15

Training set size / class

Classification accuracy

Patches

Hnd NN

Fnt NN

Img NN

FntImg NN

HndImg NN

Figure 8: Classiﬁcation results for the English datasets with

GB, SC and Patch features.

In a practical situation, all the available fonts or

hand-printed data could be used to classify images.

Table 6 shows the results obtained by training with

all available samples from Fnt and Hnd and testing

with the same Img test sets of 15 samples per class

described above. Note that for GB and SC, the NN

classiﬁcation results obtained by training with the en-

tire Fnt training set were better than those obtained

VISAPP 2009 - International Conference on Computer Vision Theory and Applications

278

Classification result

Input (test) label

Confusion Matrix

0 9 Z z

Figure 9: Confusion matrix of MKL for training and testing

on Img with 15 training samples per class.

by training with 15 Img samples per class and, in

fact, were nearly as good as MKL. This demonstrates

the generalization power of these descriptors and vali-

dates the possibility of cheaply generating large sized

synthetic sets and using them for training.

Table 6: Classiﬁcation results (%) obtained with the same

testing set as in table 5, but here the whole sets of synthetic

fonts and hand-printed characters are used for training, i.e.,

1016 and 55 samples per class, respectively.

Feature Training on Fnt Training on Hnd

GB 54.30 24.62

SC 44.84 31.08

SIFT 11.08 3.12

Patches 7.85 1.72

SPIN 3.44 2.47

MR8 1.94 1.51

Training set size 1016 55

Figure 6 shows the confusion matrix obtained for

MKL when trained and tested on 15 Img samples

per class. One can notice two patterns of high val-

ues in parallel to the diagonal line. These patterns

show that, for many characters, there is a confusion

between lower case and upper case. If we classify

characters in a case insensitive manner then the accu-

racy goes up by nearly 10%.

5.2 Kannada Data Sets

The Img dataset of Kannada characters was annotated

per symbol, which includes characters and syllables,

resulting in a set of 990 classes. Since some of these

classes occur rarely in our dataset, we did not perform

experiments training and testing with Img. Instead,

we only performed experiments on training with Hnd

characters and testing with Img. We selected a sub-

set of 657 classes which coincides with the classes

acquired for the Hnd dataset.

Table 7: Nearest neighbor results (%) for the Kannada

datasets: (i) training with 12 Hnd and testing with 13 Hnd

samples, and (ii) training with all Hnd and testing with all

Img samples.

Ftr. Trn/tst on Hnd Trn on Hnd, tst on Img

GB 17.74 2.77

SC 29.88 3.49

SIFT 7.63 0.30

Patches 22.98 0.12

Table 7 shows baseline results. For these experi-

ments, random guess would have a 0.15% accuracy.

6 CONCLUSIONS

In this paper, we tackled the problem of recognizing

characters in images of natural scenes. We introduced

a database of images of street scenes taken in Ban-

galore, India and showed that even commercial OCR

systems are not well suited for reading textin such im-

ages. Working in an object categorization framework,

we were able to improve character recognition accu-

racy by 25% over an OCR based system. The best

result on the English Img database was 55.26% and

was obtained by the multiple kernel learning (MKL)

method of (Varma and Ray, 2007) when trained using

15 Img samples per class. This could be improved fur-

ther if we were not to be case sensitive. Nevertheless,

signiﬁcant improvements need to be made before an

acceptable performance level can be reached.

Obtaining and annotating natural images for train-

ing purposes can be expensive and time consuming.

We therefore explored the possibility of training on

hand-printed and synthetically generated font data.

The results obtained by training on hand-printed char-

acters were not encouraging. This could be due to the

limited variability amongst the writing styles that we

were able to capture as well as the relatively small

size of the training set. On the other hand, using syn-

thetically generated fonts, the performance of nearest

neighbor classiﬁcation based on Geometric Blur fea-

tures was extremely good. For equivalent size train-

ing sets, training on fonts using a NN classiﬁer could

actually be better than training on the natural images

themselves. The performance obtained when training

on all the font data was nearly as good as that obtained

using MKL when trained on 15 natural image samples

per class. This opens up the possibility of harvesting

synthetically generated data and using it for training.

As regards features, the shape based features, Ge-

CHARACTER RECOGNITION IN NATURAL IMAGES

279

ometric Blur and Shape Context, consistently outper-

formed SIFT as well as the appearance based features.

This is not surprising since the appearance of a char-

acter in natural images can vary a lot but the shape

remains somewhat consistent.

We also presented preliminary results on recog-

nizing Kannada characters but the problem appears to

be extremely challenging and could perhaps beneﬁt

from a compositional or hierarchical approach given

the large number of visually distinct classes.

ACKNOWLEDGEMENTS

We would like to acknowledge the help of several vol-

unteers who annotated the datasets presented in this

paper. In particular, we would like to thank Arun,

Kavya, Ranjeetha, Riaz and Yuvraj. We would also

like to thank Richa Singh and Gopal Srinivasa for de-

veloping tools for annotation.

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