zhu 2009 pattern-recognition

8/8/2019 Zhu 2009 Pattern-Recognition

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Pattern Recognition 42 (2009) 3184 -- 3191

Contents lists available at ScienceDirect

Pattern Recognition

journal homepage: w w w . e l s e v i e r . c o m / l o c a t e / p r

Language identification for handwritten document images using a shape codebook

Guangyu Zhu , Xiaodong Yu, Yi Li, David Doermann

Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA

A R T I C L E I N F O A B S T R A C T

Article history:Received 8 August 2008

Received in revised form 24 November 2008

Accepted 21 December 2008

Keywords:

Language identification

Shape descriptor

Shape codebook

Handwriting recognition

Document image analysis

Language identification for handwritten document images is an open document analysis problem. In

this paper, we propose a novel approach to language identification for documents containing mixture of

handwritten and machine printed text using image descriptors constructed from a codebook of shape

features. We encode local text structures using scale and rotation invariant codewords, each representing

a segmentation-free shape feature that is generic enough to be detected repeatably. We learn a concise,

structurally indexed shape codebook from training by clustering and partitioning similar feature types

through graph cuts. Our approach is easily extensible and does not require skew correction, scale nor-

malization, or segmentation. We quantitatively evaluate our approach using a large real-world document

image collection, which is composed of 1512 documents in eight languages (Arabic, Chinese, English,

Hindi, Japanese, Korean, Russian, and Thai) and contains a complex mixture of handwritten and machine

printed content. Experiments demonstrate the robustness and flexibility of our approach, and show

exceptional language identification performance that exceeds the state of the art.

2009 Elsevier Ltd. All rights reserved.

1. Introduction

Language identification continues to be a fundamental research

problem in document image analysis and retrieval. For systems that

process diverse multilingual document images, such as Google Book

Search [1] or an automated global expense reimbursement applica-

tion [2], the performance of language identification is crucial for a

broad range of tasksfrom determining the correct optical character

recognition (OCR) engine for text extraction to document indexing,

translation, and search. In character recognition, for instance, almost

all existing work requires that the script and/or language of the pro-

cessed document be known [3].

Prior research in the field of language identification has focused

almost exclusively on the homogeneous content of machine printed

text. Document image collections in practice, however, often con-

tain a diverse and complex mixture of machine printed and uncon-strained handwritten content, and vary tremendously in font and

style. Language identification on document images containing hand-

writing is still an open research problem [6].

Categorization of unconstrained handwriting presents a number

of fundamental challenges. First, handwriting exhibits much larger

Corresponding author at Language and Media Processing Laboratory, Institute

for Advanced Computer Studies, University of Maryland, College Park, MD 20742,

USA. Tel.: +1 2406762573; fax: +1301 3149115

E-mail addresses: [email protected] (G. Zhu), [email protected]

(X. Yu), [email protected] (Y. Li), [email protected] (D. Doermann).

0031-3203/$- see front matter 2009 Elsevier Ltd. All rights reserved.

doi:10.1016/j.patcog.2008.12.022

variability compared to machine printed text. Handwriting varia-

tions due to style, cultural, and personalized differences are typi-cal [7], which significantly increase the diversity of shapes found

in handwritten words. Text lines in handwriting are curvilinear and

the gaps between neighboring words and lines are far from uniform.

There are no well-defined baselines for handwritten text lines, even

by linear or piecewise-linear approximation [4]. Second, automatic

processing of off-line document image content needs to be robust

in the presence of unconstrained document layouts, formatting, and

image degradations.

Our approach is based on the view that the intricate differences

between languages can be effectively captured using low-level

segmentation-free shape features and structurally indexed shape

descriptors [25]. Low-level local shape features serve well suited

for this purpose because they can be detected robustly in practice,

without detection or segmentation of high-level entities, such astext lines or words. As shown in Fig. 2, visual differences between

handwriting samples across languages are well captured by differ-

ent configurations of neighboring line segments, which provide rich

description of local text structures.

We propose a novel approach for language identification using

image descriptors built from a codebook of generic shape features

that are translation, scale, and rotation invariant. To construct

structural index among a large number of shape features extracted

from diverse content, we dynamically partition the space of shape

primitives by clustering similar feature types. We formulate fea-

ture partitioning as a graph cuts problem with the objective of

obtaining a concise and globally balanced index by sampling from
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G. Zhu et al. / Pattern Recognition 42 (2009) 3184 -- 3191 3185

Fig. 1. Examples from the Maryland multilingual handwriting database [4] and IAM handwriting DB3.0 database [5]. Languages in the top row are Arabic, Chinese, English,

and Hindi, and those in the second row are Japanese, Korean, Russian, and Thai.

Fig. 2. Shape differences in text are captured locally by a large variety of neighboring line segments. (a)(d) Examples of handwriting from four different languages. (e)(h)

Detected line segments after local fitting, each shown in different color.

training data. Each cluster in the shape codebook is represented

by an exemplary codeword, making association of the feature type

efficient. We obtain very competitive language identification per-

formance on real document collections using a multi-class SVM

classifier.

The structure of this paper is as follows. Section 2 reviews re-

lated work. In Section 3, we describe the algorithm for learning the

codebook from diverse low-level shape features, and present a com-

putational framework for language identification using the shape

codebook. We discuss experimental results in Section 4 and conclude

in Section 5.

2. Related work

We first present a comprehensive overview of existing techniques

on script and language identification and discuss their limitations on

document images with unconstrained handwriting. We then high-

light work related to our approach on contour-based learning.

2.1. Language identification

Prior literature on script and language identification has largely

focused on the domain of machine printed documents. These works


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3186 G. Zhu et al. / Pattern Recognition 42 (2009) 3184 -- 3191

can be broadly classified into three categoriesstatistical analysis of

text lines [812], texture analysis [13,14], and template matching

[15].

Statistical analysis using discriminating features extracted from

text lines, including distribution of upward concavities [8,9], hori-

zontal projection profile [10,11], and vertical cuts of connected com-

ponents [12], has proven to be effective on homogeneous collection

of machine printed documents. These approaches, however, do have

a few major limitations for handwriting. First, they are based on theassumption of uniformity among printed text, and require precise

baseline alignment and word segmentation. Freestyle handwritten

text lines are curvilinear, and in general, there are no well-defined

baselines, even by linear or piecewise-linear approximation [4]. Sec-

ond, it is difficultto extendthesemethods to a newlanguage, because

they employ a combination of hand-picked and trainable features

and a variety of decision rules. In fact, most of these approaches re-

quire effective script identification to discriminate between selected

subset of languages, and use different feature sets for script and lan-

guage identification, respectively.

Script identification using rotation invariant texture features,

including multi-channel Gabor filters [13] and wavelet log co-

occurrence features [14], were demonstrated on small blocks of

printed text with similar characteristics. However, no results werereported on full-page documents that involve variations in layouts

and fonts. Script identification for printed words was explored in

[16,17] using texture features between a small number of scripts.

Template matching approach computes the most likely script by

probabilistic voting on matched templates, where each template pat-

tern is of fixed size and is rescaled from a cluster of connected com-

ponents. The script and language identification system developed

by Hochberg et al. [15] at Los Alamos National Laboratory based

on template matching is able to process 13 machine printed scripts

without explicit assumptions of distinguishing characteristics for a

selected subset of languages. Template matching is intuitive and de-

livers state-of-the-art performance when the content is constrained

(i.e. printed text in similar fonts). However, templates are not flexi-

ble enough to generalize across large variations in fonts or handwrit-ing styles that are typical in diverse datasets [15]. From a practical

point of view, the system also has to learn the discriminability of

each template through labor-intensive training to achieve the best

performance. This requires tremendous amount of supervision and

further limits applicability.

There exists very little literature on language identification for

unconstrained handwriting. To the best of our knowledge, the exper-

iments of Hochberg et al. [7] on script and language identification of

handwritten document images is the only reference on this topic in

the literature. They used linear discriminant analysis based on five

simple features of connected component, including relative centroid

locations and aspect ratio. The approach is shown to be sensitive

to large variations across writers and diverse document content [7].

Irregularities in the document, including machine printed text, il-

lustrations, markings, and handwriting in different orientation from

main body, were removed manually by image editing from their

evaluation dataset.

2.2. Contour-based learning

Learning contour features is an important aspect in many com-

puter vision problems. Ferrari et al. [18] proposed scale-invariant ad-

jacent segment (kAS) features extracted from the contour segment

network of image tiles, and used them in a sliding window scheme

for object detection. By explicitly encoding both geometric and spa-

tial arrangement among the segments, kAS descriptor demonstrates

state-of-the-art performance in shape-based object detection, and

outperforms descriptors based on interest points and histograms of

gradient orientations [19]. However, kAS descriptor is not rotation

invariant, because segments are rigidly ordered from left to right.

This limits the repeatability of high-order kAS, and the best perfor-

mance in [18] is reported when using 2AS.

In handwriting recognition literature, one interesting work also

motivated by the idea of learning a feature codebook is the study

on writer identification by Schomaker et al. [20]. Writer identifi-

cation assumes that the language is known beforehand and aims

to distinguish between writers based on specific characteristics ofhandwriting. Schomaker et al. used closed-contour features of ink

blobs directly, without any shape descriptor. The difference between

contour features is computed using Hamming distance. The low-

dimensional codebook representation presented in [20] is based on

Kohonen self-organizing map (SOM) [21]. Their approach demands

good segmentation and is not scale or rotation invariant. To account

for size variations, for instance, SOMs need to be computed at mul-

tiple scales, which requires large training data and reportedly takes

122 hours on a 128-node Linux cluster.

3. Constructing a shape codebook

Recognition of diverse text content needs to account for large

intra-class variations typical in real document collections, includ-ing unconstrained document layouts, formatting, and handwriting

styles. The scope of the problem is intuitively in favor of low-level

shape features that can be detected robustly without word or line

detection and segmentation. Rather than focusing on selection of

class-specific features, our approach aims to distinguish differences

between text collectively using the statistics of a large variety of

generic, geometrically invariant feature types (shape codewords)

that are structurally indexed. Our emphasis on the generic nature of

codewords presents a different perspective to recognition that has

traditionally focused on finding sophisticated features or visual se-

lection models, which may limit generalization performance.

We explore the kAS contour feature recently introduced by Ferrari

et al. [18], which consists of a chain ofk roughly straight, connected

contour segments. Specifically, we focus on thecase of triple adjacentsegments (TAS) [22], which strike a balance between lower-order

contour features that are not discriminative enough and higher-order

ones that are less likely to be detected robustly. As shown in Fig. 3,

a contour feature C is compactly represented by an ordered set of

lengths and orientations of ci for i {1,2,3}, where ci denotes line

segment i in C.

3.1. Extraction of contour features

Feature detection in our approach is very efficient. First, we com-

pute edges using the Canny edge detector [23], which gives precise

localization and unique response on text content. Second, we group

contour segments by connected components and fit them locally

into line segments using an algorithm that breaks a line segmentinto two only when the deviation is beyond a threshold [24]. Then,

within each connected component, every triplet of connected line

segments that starts from the current segment is extracted. Fig. 2

provides visualization of the quality of detected contour features by

our approach using randomly permuted colors for neighboring line

segments.

Our feature detection scheme requires only linear time and space

in the number of contour fragments n, and is highly parallelizable. It

is more efficient and stable than [18], which requires construction of

contour segment network and depth first search from each segment,

leading to O(n log(n)) time on average and O(n2) in the worst case.

We encode text structures in a translation, scale, and rotation

invariant fashion by computing orientations and lengths with ref-

erence to the first detected line segment. This is distinct from the
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Fig. 3. Examples of (a) C-shape, (b) Z-shape and (c) Y-shape triple adjacent segment (TAS) shape features that capture local structures. TAS shape descriptors are

segmentation-free, and are translation, scale and rotation invariant.

Fig. 4. The 25 most frequent exemplary codewords for (a) Arabic, (b) Chinese, (c) English, and (d) Hindi document images, capture the distinct shape features of different

languages. (e)(h) show codeword instances in the same cluster as the first five exemplary codewords for each language (the leftmost column), ordered by ascending

distances to the center of their associated clusters. Scaled and rotated versions of similar features are clustered together.

motivation ofkAS descriptor that attempts to enumerate spatial ar-

rangements of contours within local regions. Furthermore, kAS de-

scriptor does not take into account of rotation invariance.

3.2. Measure of dissimilarity

The overall dissimilarity between two contour features can be

quantified by the weighted sum of the distances in lengths and ori-

entations. We use the following generalized measure of dissimilaritybetween two contour features Ca and Cb

d(Ca,Cb, k) = kTlengthdlength + k

Torientdorient, (1)

where vectors dlength and dorient are composed of the individual dis-

tances between contour lengths and orientations, respectively.klengthand korient are their corresponding weight vectors, providing sensi-

tivity control over the tolerance of line fitting. One natural measure

of dissimilarity in lengths between two contour segments is their

log ratio. We compute orientation difference between two segments

by normalizing their absolute value of angle difference to . In our

experiments, we use a larger weighting factor for orientation to de-

emphasize the difference in the lengths because they may be less

accurate due to line fitting.

3.3. Learning the shape codebook

We extract a large number of shape codewords by sampling

from training images, and construct an indexed shape codebook by

clustering and partitioning the codewords. A codebook provides a

concise structural organization for associating large varieties of low-

level features [25], and is efficient because it enables comparison to

much fewer feature types.

3.3.1. Clustering shape features

Prior to clustering, we compute the distance between each pair

of codewords and construct a weighted graph G = (V,E), in which

each node on the graph represents a codeword. The weight w(Pa,Pb)

on an edge connecting two nodes Ca and Cb is defined as a function

of their distance

w(Ca,Cb) = exp

d(Ca,Cb)

2

2D

, (2)

where we set parameter D to 20% of the maximum distance among

all pairs of nodes.

We formulate feature clustering as a spectral graph partitioning

problem,in whichwe seek togroup the set ofvertices Vinto balanced

disjoint sets {V1,V2, . . . ,VK}, such that by the measure defined in (1),


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the similarity between vertices within the same set is high and that

between different sets is low.

More concretely, let the NNsymmetric weight matrix for all the

vertices be Wand N= |V|. We define the degree matrix D as an NN

diagonal matrix, whose i-th element D(i) along the diagonal satisfies

D(i) =

jW(i,j). We use an N K matrix X to represent a graph

partition, i.e. X= [X1,X2, . . . ,XK], where each element of matrix X is

either 0 or 1. We can show that the feature clustering formulation

that seeks a globally balanced graph partition is equivalent to thenormalized cuts criterion [26], and can be written as

maximize (X) =1

K

Kl=1

XTlWXl

XTlDXl

, (3)

subject to X {0, 1}NK andj

X(i,j) = 1. (4)

Minimizing normalized cuts exactly is NP-complete. We use a fast

algorithm [27] for finding its discrete near-global optima, which is

robust to random initialization and converges faster than other clus-

tering methods.

3.3.2. Organizing features in the codebook

For each cluster, we select the feature instance closest to thecenter of the cluster as the exemplary codeword. This ensures that

an exemplary codeword has the smallest sum of squares distance to

all the other features within the cluster. To effectively capture the

variations among each feature type, we compute the cluster radius of

each exemplary codeword. The radius of the cluster is defined as the

maximum distance from the cluster center to all the other features

within the cluster. The constructed codebook C is composed of all

exemplary codewords.

Table 1

Languages and varieties in dataset used for evaluation of language identification.

Language Arabic Chinese English Hindi Japanese Korean Russian Thai

Pages 245 184 175 186 161 168 204 189

Writers 22 18 26 17 8 25 5 10

Table 2

Confusion table of our approach for the eight languages.

A C E H J K R T

A 99.7 0.3 0 0 0 0 0 0

C 1.4 85.0 4.0 1.0 6.7 1.0 0.7 0.2

E 1.6 0 95.9 0.2 0 1.1 0.6 0.6

H 0.2 0.2 0 98.8 0.8 0 0 0

J 0 1.3 1.0 0.2 96.2 1.3 0 0

K 0 0.8 0.1 1.9 0.5 96.0 0.5 0.1

R 0.5 0 2.0 0 0 0 97.1 0.4

T 0 0.3 1.6 0.9 0.6 0.3 0 96.3

Fig. 5. Confusion tables for language identification using (a) LBP [28], (b) template matching [7], (c) kAS [18], (d) our approach (A: Arabic, C: Chinese, E: English, H: Hindi,

J: Japanese, K: Korean, R: Russian, T: Thai, U: Unknown).

Fig. 4 shows the 25 most frequent exemplary shape codewords

for Arabic, Chinese, English, and Hindi, which are learned from 10

documents of each language. Distinguishing features between lan-

guages, including cursive style in Arabic, 45 and 90 transitions in

Chinese, and various configurations due to long horizontal lines

in Hindi, are learned automatically. Each row in Fig. 4(e)(h) lists

examples of codewords in the same cluster, ordered by ascending

distances to the center of their associated clusters. Through cluster-

ing, translated, scaled and rotated versions of similar features aregrouped together.

Since each codeword represents a generic local shape feature, in-

tuitively a majority of codewords should appear in images across

content categories, even though their frequencies of occurrence devi-

ate significantly. In our experiments, we find that 86.3% of codeword

instances appear in document images across all eight languages.

3.4. Constructing the image descriptor

We construct a histogram descriptor for each image, which pro-

vides statistics of the frequency at which each feature type occurs.

For each detected codeword W from the image, we compute the

nearest exemplary word Ck in the shape codebook. We increment

the descriptor entry corresponding to Ck only if

d(W,Ck)< rk, (5)

where rk is the cluster radius associated with the exemplary code-

word Ck. This quantization step ensures that unseen features that

deviate considerably from training features are not used for image

description. In our experiments, we found that only less than 2% of

the contour features cannot be found in the shape codebook learned

from the training data.

4. Experiments

4.1. Dataset

We use 1512 document images of eight languages (Arabic, Chi-nese, English, Hindi, Japanese, Korean, Russian, and Thai) from the

University of Maryland multilingual database [4] and IAM handwrit-

ing DB3.0 database [5] for evaluation on language identification (see

Fig. 1). Both databases are well-known, large, real-world collections,

which contain the source identity of each image in the ground truth.

This enables us to construct a diverse dataset that closely mirrors the

true complexities of heterogeneous document image repositories in

practice. Table 1 summarizes the statistics of the dataset.

4.2. Overview of the experiments

We compare our approach with three state-of-the-art ap-

proaches that are well motivated by different views of image


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categorization template matching [15], local binary patterns (LBP)

[28], and kAS [18]. Proposed by Ojala et al. [28], LBP is a widely used

rotation invariant approach for texture analysis. It captures spatial

structure of local image texture in circular neighborhoods across

angular space and resolution, and has demonstrated excellent results

in a wide range of whole-image categorization problems involving

diverse data [29,30].

For effective comparison, we used multi-class SVM classifiers [31]

trained on the same pool of randomly selected handwritten docu-ment images from each language class in the following experiments,

for LBP, kAS, and our approach, respectively. We further evaluate the

generalization performance of our approach as the size of training

data varies.

4.3. Results and discussions

The confusion tables for LBP, template matching, kAS, and our

approach are shown in Fig. 6. Our approach gives excellent results

on all the eight languages, with a mean diagonal of 95.6% and a

standard deviation of 4.5%. Table 2 lists all entries in the confusion

table of our approach for the eight languages.

kAS, with a mean diagonal of 88.2%, is also shown to be very

effective. Neither kAS nor our approach has difficulty generalizingacross large variations such as font types or handwriting styles, as

evident from their relatively small standard deviations along diag-

onal entries in the confusion tables, compared to LBP and template

matching (see Fig. 6).

The performance of template matching varies significantly across

languages, with 68.1% mean diagonal and 20.5% standard deviation

Fig. 6. Comparison of language identification performances of different approaches.

Fig. 7. Examples of error cases of Chinese handwritten documents due to image degradations and content mixture.

along diagonal. One big confusion of template matching is between

Japanese and Chinese since a document in Japanese may contain

varying number of Kanji (Chinese characters). Rigid templates are

not flexible for identifying discriminative partial features and the

bias in voting decision towards the dominant candidate causes less

frequently matched templates to be ignored. Another source of error

that lowers the performance of template matching is undetermined

cases (see the unknown column in Fig. 5(b)), where probabilistic

voting cannot decide between languages with roughly equal votes.Texture-based LBP could not effectively recognize differences be-

tween languages on a diverse dataset because distinctive layouts and

unconstrained handwriting exhibit irregularities that are difficult to

capture using texture, and its mean diagonal is only 55.1%.

Fig. 6 quantitatively compares the overall language identification

performances of different approaches by the means and standard

derivations of diagonal entries in their confusion tables. Shape-based

approaches, including our approach and kAS, show higher recogni-

tion rates and smaller performance derivations, compared to those

based on models of textures and rigid templates.

It gives further insights to analyze the language identification er-

rors made by our approach. Among all the eight languages, recog-

nition of Chinese handwriting is shown to be the most challenging

task. As shown in Fig. 5, the Chinese language identification per-formances for all the four approaches are significantly lower com-

pared to those of other languages. We show several typical error

cases in Fig. 7, where Chinese handwritten documents are wrongly

Fig. 8. Recognition rates of our approach as the size of training data varies. Our

approach achieves excellent language identification performance even using a small

number of handwritten document images per language for training.
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recognized as other languages. These error cases include document

images with severe image degradations, second-generation docu-

ments captured from low-resolution source copies (e.g. photocopies

of fax documents), and documents containing significant amount of

other content.

Good generalization is important for the success of document

analysis systems that need to process diverse, unconstrained data.

Fig. 8 shows the recognition rates of our approach as the size of train-

ing set varies. We observe highly competitive language identificationperformance on this challenging dataset even when a small amount

of training data per language class is used. This demonstrates the

effectiveness of using generic low-level shape features when mid or

high-level vision representations may not be generalized or flexible

enough for the task.

Our results on language identification are very encouraging from

a practical point of view, as the training in our approach requires

considerably less supervision. Our approach only needs the class

label of each training image, and does not require skew correction,

scale normalization, or segmentation.

5. Conclusion

In this paper, we have proposed a computational framework forlanguage identification using a shape codebook composed of a wide

variety of local shape features. Each codeword represents a charac-

teristic structure that is generic enough to be detected repeatably,

and the entire shape codebook is learned from training data with

little supervision. Low-level, segmentation-free shape features and

the geometrically invariant shape descriptor improves robustness

against large variations typical in unconstrained handwriting, and

makes our approach flexible to extend. In addition,the codebook pro-

vides a principled approach to structurally indexing and associating

a vast number of feature types. Experiments on large complex real-

world document image collections show excellent language identi-

fication results that exceed the state of the art.

Acknowledgments

Portions of this paper appeared in previous conference publica-

tions [25,32]. This research was supported by the US Department of

Defense under contract MDA-9040-2C-0406. The authors would like

to thank the anonymous reviewers for their constructive comments,

which have significantly improved the paper.

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About the AuthorGUANGYU ZHU received the B.Eng. (First Class Honours) in Electrical and Electronic Engineering from Nanyang Technological University, Singapore, in2001. He is currently a Ph.D. candidate in the Department of Electrical and Computer Engineering at University of Maryland, College Park. From 2001 to 2003, he wasa technical solution architect at IBM Singapore. He worked at IBM Almaden Research Center, San Jose, California, in 2006 and Yahoo! Search and Advertising Sciences,Santa Clara, California, in 2008. Since 2004, he has been a member of the Language and Media Processing Laboratory, University of Maryland. His research interests includedocument image analysis, machine learning, pattern recognition, and information retrieval. He is a member of the IEEE and IAPR.
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About the AuthorXIAODONG YU received the B.Eng. in Electrical Engineering from University of Science and Technology of China and M.S. in Computer Science fromNational University of Singapore in 1999 and 2002, respectively. He was with the Institute for Infocomm Research, Singapore, and the Nanyang Technological University,Singapore, from 2001 to 2004. He is now a Ph.D. student in the Department of Electrical and Computer Engineering, University of Maryland, College Park. His researchinterests include the object recognition, image and video retrieval. He is a member of the IEEE.

About the AuthorYI LI received the B.Eng. and M.Eng. degrees from South China University of Technology, Guangzhou, China, in 2001 and 2004, respectively. He iscurrently working toward the Ph.D. degree in the Department of Electrical and Computer Engineering at the University of Maryland, College Park. He is with the MarylandTerrapin Team, which received Second Place in the First Semantic Robot Vision Challenge (SRVC) at the 22nd Conference on Artificial Intelligence (AAAI 2007) sponsored bythe US National Science Foundation. His research interests include document image processing, shape analysis, object categorization, robotics, and computer vision. He isthe Future Faculty Fellow of the A. James Clark School of Engineering at the University of Maryland. He received the Best Student Paper Award from the 10th International

Workshop on Frontiers in Handwriting Recognition (IWFHR 2006). He is a student member of the IEEE.

About the AuthorDAVID DOERMANN received the B.S. degree in Computer Science and Mathematics from Bloomsburg University in 1987 and the M.S. and Ph.D.degrees from the University of Maryland, College Park, in 1989 and 1993, respectively. Since 1993, he has been a codirector of the Laboratory for Language and MediaProcessing in the Institute for Advanced Computer Studies at the University of Maryland and an adjunct member of the graduate faculty. His team of 1520 researchersfocuses on topics related to document image analysis and multimedia information processing. The team's recent intelligent document image analysis projects include pagedecomposition, structural analysis and classification, page segmentation, logo recognition, document image compression, duplicate document image detection, image-basedretrieval, character recognition, generation of synthetic OCR data, and signature verification. In video processing, projects have centered on the segmentation of compresseddomain video sequences, structural representation and classification of video, detection of reformatted video sequences, and the performance evaluation of automated videoanalysis algorithms. He is a coeditor of the International Journal on Document Analysis and Recognition. He has more than 25 journal publications and almost 100 refereedconference proceedings. He is a member of the IEEE.

zhu 2009 pattern-recognition

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