A Scene Text-Based Image Retrieval System

Transcription

1 A Scene Text-Based Imae Retrieval System Thuy Ho Faculty of Information System University of Information Technoloy Ho Chi Minh city, Vietnam Noc Ly Faculty of Information Technoloy University of Science Ho Chi Minh city, Vietnam Abstract The rapidly increasin popularity of diital imaes raise a question of how to automatically index and help users naviate such libraries of imaes. One approach is to extract text appearin in imaes which often ives an indication of a scene's semantic content. However, it can be challenin since the text is often embedded in a complex backround. This paper presents a novel approach for queryin imaes usin scene text. The text in natural scene imaes is localized and extracted properly by a novel mechanism. Then, an indexin and retrieval scheme is introduced. The proposed methods are evaluated on the public database of the ICDAR 2003 competition. Experimental results are very encourain and suest that these alorithms can be used in imae retrieval applications. Keywords text detection, text binarization, scene text, imae retrieval, imae indexin I. INTRODUCTION With the widely use of diital imae capture devices, such as diital cameras, mobile phones and PDAs, quantities of available imaes are rapidly increasin. This increasin availability has rekindled interest in the problem of how to index multimedia information sources automatically as well as how to browse and manipulate them efficiently. Traditionally, imaes have been manually annotated with a small number of keywords descriptors. Unfortunately, comparin the volume of imae files available at the current time, it is difficult to extract information from these files manually throuh human intervention. This has motivated many researchers to desin and develop new alorithms to index and store imae on a database for faster retrieval. Amon all contents in imaes, such as face, human, scene, etc., text is found to be one of the most important features to understand the imae content and has been used to index and retrieve imaes. If the text in an imae can be extracted, it can provide natural, meaninful keywords indicatin the imae s content. As an essential prerequisite for scene text-based imae search, text within imaes has to be robustly located. Traditional OCR based methods are not directly applicable to imaes of text. This is mainly because of the difficulties in detectin texts in imaes. The majority of OCR enines is desined for scanned text and so depends on sementation which correctly separates text from backround pixels. While this is usually simple for scanned text, it is much harder in natural imaes. Natural imaes are usually at lower resolution and have added complexities due to the variations of font, size, color, alinment, and lihtin condition. In addition, text is /12/$ IEEE often embedded in a complex backround, liht shadow, nonplanar objects. Althouh some existin methods have reported promisin results, there still remain several difficult problems need to be solved. Most of the previous studies in text detection can be classified into approaches based on ede, connected component, and texture [1]. Ede based methods are focused on tryin to find out reions on the imae where there is a hih contrast between text and backround in order to detect and to mere edes from letters in imaes [2][3]. Ede based methods is fast and can have a hih recall. However, it often produces many false alarms since the backround may also have stron edes similar to the text. Texture based methods [4][5][6] classify text reions from non-text reions usin texture features. The texture based methods mostly use texture analysis approaches such as Gaussian filterin, Wavelet decomposition, Fourier transform, Discrete Cosine Transform (DCT) or Gabor filterin in order to obtain texture information from imaes. Machine learnin methods are often used in this approach. One problem of this approach is that some texture features are of hih dimensions, addin to the complexity of classification. Connected components (CCs) based methods [7][8] use bottom-up approach which is based on iteration to mere and combine connected pixels by the help of homoeneity criterion. These methods normally include four steps: i) pre-processin, such as color clusterin and noise reduction, ii) CC eneration, iii) filterin out non-text components and iv) component roupin [1]. Compared with the texture based approaches, the CC based approaches have the advantaes of quicker computation and less sensitive to font size. However, their performances drop dramatically when detectin texts in complex backrounds. Numerous reports have been published about indexin and retrieval of diital imaes, each concentratin on different aspects (see [9]). Automatic imae indexin enerally uses indices based on the color, texture, or shape of objects. Other systems are restricted to specific domains such as newscasts, or soccer. None of them tries to extract and reconize automatically the text appearin in diital imaes and use it as an index for retrieval. In this paper, we present a new approach to imae retrieval usin scene text. In which, the text in natural scene imaes is detected and reconized by a novel method. We also demonstrate that the proposed methods enable semantic indexin and retrieval. Overall, our paper offers the followin main contributions:

2 Report Documentation Pae Form Approved OMB No Public reportin burden for the collection of information is estimated to averae 1 hour per response, includin the time for reviewin instructions, searchin existin data sources, atherin and maintainin the data needed, and completin and reviewin the collection of information. Send comments reardin this burden estimate or any other aspect of this collection of information, includin suestions for reducin this burden, to Washinton Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Hihway, Suite 1204, Arlinton VA Respondents should be aware that notwithstandin any other provision of law, no person shall be subject to a penalty for failin to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE DEC REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE A Scene Text-Based Imae Retrieval System 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Faculty of Information System University of Information Technoloy Ho Chi Minh city, Vietnam 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR S ACRONYM(S) 12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release, distribution unlimited 11. SPONSOR/MONITOR S REPORT NUMBER(S) 13. SUPPLEMENTARY NOTES See also ADA IEEE International Symposium on Sinal Processin and Information Technoloy (12th) (ISSPIT 2012) Held in Ho Chi Minh City, Vietnam on December 12-15, AOARD-CSP ABSTRACT The rapidly increasin popularity of diital imaes raise a question of how to automatically index and help users naviate such libraries of imaes. One approach is to extract text appearin in imaes which often ives an indication of a scene s semantic content. However, it can be challenin since the text is often embedded in a complex backround. This paper presents a novel approach for queryin imaes usin scene text. The text in natural scene imaes is localized and extracted properly by a novel mechanism. Then, an indexin and retrieval scheme is introduced. The proposed methods are evaluated on the public database of the ICDAR 2003 competition. Experimental results are very encourain and suest that these alorithms can be used in imae retrieval applications. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR a. REPORT unclassified b. ABSTRACT unclassified c. THIS PAGE unclassified 18. NUMBER OF PAGES 6 19a. NAME OF RESPONSIBLE PERSON Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18

3 We propose a new localization-verification scheme to robustly detect text strins with variations of font style, size, color and scale from complex natural scene imaes. Our system provides a reliable binarization for the detected text, which can be passed to OCR for text reconition. We propose a new approach to imae retrieval usin scene text. The our proposed model offers to support users to search for imaes even when it is unable to input keywords due to some cause (for instance not knowin the lanuae of keywords). The remainder of paper is oranized as follows. Section II shows our approach overview. Section III provides a detailed description of our alorithms for text detection and reconition. In section IV, we introduce a indexin and retrieval scheme. Section V shows experimental results. In section VI, we draw conclusions. II. SYSTEM OVERVIEW The overall process of the text detection and extraction is illustrated in Fi. 1. Our method is composed of two parts: text localization and text verification. The first part (text localization) consists of two steps: pre-processin and eneration of candidate text reions. At pre-processin step, a procedure based on reconstruction transform is applied to remove noise as non-text reions. Next, ede features and morpholoical dilation are employed to locate imae blocks. We apply Stroke Width Transform from [10] with some modification to enerate candidate letters. These letters are paired to identify text lines, which are subsequently separated into words. At text verification stae, we use Historam of Oriented Gradient (HOG) features to train a Support Vector Machines (SVM)-based classifier to determine whether a candidate word is text or not. Then the text reions are extracted by a novel binarization alorithm. The binarized text is reconized by OCR software. We apply a simple OCR postcorrection model on the OCR output to improve the reconition performance. Finally, the reconized words are used as query keywords for retrieval. Text Localization A. Pre-processin III. TEXT DETECTION AND RECOGNITION Generally, the color of the text in an imae is lihter or darker as compared to its backround. For filterin out non-text reions, we apply reconstruction transformation on the ray scale imae. In natural scene imaes, the information to be athered should be within imae not in the border reions of the imae. As this step is used to remove objects connected to borders and lihter than it s surroundin. The reconstruction by dilation of a mask imae from a marker imae f D D and f ) is defined as the eodesic dilations of ( f f with respect to iterated until stability and is denoted by R ( f) : R f f (1) () i ( ) ( ) ( i) ( i 1) where i is such that ( f) ( f) [11]. We implement the reconstruction based on alorithm described in [12]. The detail of the alorithm can be found in that paper. Accordin to Soille [11], the reconstruction transformation can be used to extract connected imae objects havin hiher intensity values than the surroundin objects. The marker imae equals then zero everywhere except the points x which have a value equal to that of the imae at the same position. In order to remove objects connected to the imae border, we use the input imae as mask imae and the marker imae zero everywhere except alon its border where the values of the mask imae are considered. The removal of objects connected to the imae border is illustrated in Fi. 2. Then we apply a threshold in the reconstructed imae to produce a binary one. After this processin step, many non-text reions are removed. We have seen that this process reduce noise and enhance the text reions of the imae. B. Generation of candidate text reions To reduce noise and connect strokes in the binary imae, we use two morpholoical operators: closin operation and dilation operation. In low contrast imaes, a character sometimes miht be broken to pieces. Thus, it is more likely that connected component analysis miht make the wron decision. So, we have to mere these reions first. Text Verification Input Imae Pre- Processin Generate candidate reions Areation Word Verification Text Extraction Binarized text Convert to Grayscale SWT Text line eneration SVM classifier Local Binarization Reconstruction Component Filterin Word Separation Fi. 1. The flowchart of the proposed text detection and extraction method

4 (a) (b) (c) Fi. 2. (a) Gray scale imae, (b) Reconstruction of from f : R ( f), (c) R ( f ) Here, we utilize closin operator with a structurin element to the binary imae obtained from the previous step to solve this problem (Fi. 3. (b)). A morpholoical dilation operator can easily connect the very close reions toether while leavin those whose positions are far away to each other isolated. In our proposed method, we use a morpholoical dilation operator with a 33 1 structurin element to et joint areas referred to as text blobs. By applyin eometrical constraints such as block heiht (reater than 8 pixels), aspect ratio (not lower than 0.5), we et candidate text blocks. The reion whose size is too small will also be eliminated. The candidate reion is shown in Fi. 3. (c). (a) (b) (c) Fi. 3. Generation of candidate reions. (a) binary imae, (b) closin on a), (c) dilation on (b) 1) Component Extraction Motivated by Epshtein s work on the Stroke Width Transform (SWT), we apply SWT to form connected components. Epshtein et al. [10] have proved that the stroke width feature is robust to distinuish text from other complex objects that are visually similar to text such as veetation. The output of the SWT is an imae of size equal to the size of the input imae where each element contains the width of the stroke associated with the pixel (see [10] for details). Considerin the effectiveness and efficiency, the computation of SWT can be quite expensive if the imae has complex content. Thus, we only apply SWT on the detected candidate text blocks. Fi. 4. shows the result of stroke width transform, where a darker color corresponds to a shorter stroke width. The next stae is formin connected components. The character components can be enerated by merin pixels with similar stroke width value. We do this by considerin two pixels as neihborin if the ratio of the stroke widths does not exceed ) Component filterin We use some of the heuristic rules to filter the connected components. The most important rule is that we filter components based on the ratio of their stroke width standard deviation (std) to their stroke width mean. The rejection criterion is std/mean > 0.5, which is invariant to scale chanes. This threshold was obtained from the trainin set of the ICDAR competition database. Another important rule is checkin if a component s boundin box contains more than three other component s centers, which eliminates sins and frames. Components whose size is too small or too lare may be inored. C. Group components into word 1) Text line areation The first step of merin consists of roupin adjacent letters in order to form text line. Text lines are important cues for the existence of text, as text almost always appear in the form of straiht lines or sliht curves. This reasonin allows us to determine whether the connected components belon to text characters or unexpected noises. We first pairwise roup the letter candidates usin the followin rules: i) the ratio of their stroke width medians is lower than 1.5, ii) considerin the upper and lower case letters, their heiht ratio is lower than 2.25, iii) the distance between two letters should not be reater than three times the width of the wider one, iv) the difference between y-coordinates of the connected component centroids should not be reater than 0.5 times the heiht of the hiher one. Subsequently, text lines are formed based on clusters of pairwise connected letter candidates. We mere toether two roups if they share one end and have similar direction. The process is iterated until all text candidates have been assined to a line, or if there are less than three candidates available within the cluster. A line is declared to be a text line if it contains three or more text objects. The output of this step is illustrated in Fi. 5. (b). (a) (b) (c) Fi. 5. (a) Connected components, (b) Text line boundin boxes, (c) Word boundin boxes Fi. 4. Stroke width transform result 2) Word separation The aim of this step is to separate mered letters into words. To split text lines into words we propose to use the followin process which is based on the computation of distances between boundin boxes of letters detected in the previous step

5 (see Fi. 6. ). Based on the statistics of the distance distribution (mean and standard deviation values) over the line, a threshold is computed from (2) to split the mered letters in words. T Mean( D) Std( D) (2) Threshold T stands for decision whether to split a roup of letters or no. We build the distance vector D by measurin the horizontal distances between letters. If the distance between two letters exceeds the threshold, we consider that the two letters belons to two different words hence they are split. In our system, we set 1.5. The output of this step is illustrated in Fi. 5. (c). C1 D. Word Verification Fi. 6. Distance between two boundin boxes In order to reject falsely detected words, we have developed a verification procedure. We select SVM as classifier in the work. SVM is proposed by Vapnik [13] and have yield excellent results in various binary classification problems in recent years. The important advantaes of the support vector classifiers is that it offers a possibility to train eneralizable, nonlinear classifiers in hih-dimensional spaces usin small trainin set. In this paper, an SVM classifier with the RBF kernel was used. HOG has been widely used in computer vision. HOG feature shows better performance in characterizin object shape and appearance and it is not sensitive to illumination chane. To calculate the HOG feature, the radient vector of each imae pixel within a predefined local reion is calculated by usin Sobel operator. A historam is subsequently created by usin accumulated radient vectors, and each historam-bin is set to correspond to a radient direction. In order to avoid sensitivity of the HOG feature to illumination, the feature values are often normalized. In this work, we use HOG features to train the SVM classifier for text classification. The SVM was trained on a dataset consistin of 1156 text reions and 800 non-text reions. All trainin samples were normalized to (width heiht) as shown in Fi. 7. In the experiment, we are adopted block size is 8 8, cell size is 2 2 and 9 bins for historam. For each candidate word, HOG feature is extracted and used by the SVM classifier to verify whether it is a true word. Text samples Distance C2 Non-text samples Fi. 7. Some samples from trainin data E. Text extraction Text extraction is an important stae before character reconition. Binarized text reions are used as input to the OCR readin stae. It has been found that lobal thresholdin is not ideal for camera-captured imaes due to lihtin variation. In this paper, we propose a binarization method based on SWT imae to extract characters from text line imae. The formula to binarize each pixel is defined as follows: bx ( ) 255 if T ray( x) T 1 2 (3) 0 other where T mean( R) k std( R) and 1 T mean( R) k std( R). mean( R ) and std( R ) are the 2 intensity mean and standard deviation of the pixel whose stroke width value is reater than zero in the text reion R. The smoothin term k is set to 1.5 in practical. F. Text reconition For text reconition, we apply Goole s open source OCR Tesseract on the binary imae. OCR software has many uses pertainin to the processin and archival of printed documents, but its application to photoraphic imaes of text still remains challenes. In the paper, we propose a simple OCR postcorrection method to improve the accuracy of text reconition in natural scene imaes. Followin the ideas presented in [14], we use Levenshtein distance to choose candidates from the dictionary D selected for correction. The Levenshtein distance between two strins is defined as the minimum number of edits needed to transform one strin into the other, with the allowable edit operations bein insertion, deletion, or substitution of a sinle character. Given a word w received from the OCR, we compute the Levenshtein distance between w and each word in the dictionary and retain only those ocr words with the lowest Levenshtein distance as correction candidates. Given word w a a a, a word score sw ( ) is 1 2 n defined as follows: s( w) n2 n2 Pa ( a a ) (4) i2 i i1 i1 The probability P( a a a ) is estimated usin relative i2 i i 1 frequency of the sequence in the dictionary. For each candidate word w, we calculate sw ( ). The candidate cand cand word with the hihest score sw ( ) is selected. cand IV. INDEXING AND RETRIEVAL The indexin scheme is quite simple. The imaes will be automatically annotated by usin the words reconized. We allow users to query in two ways: by keywords and by imaes containin the desired keywords. The difference in query by keywords is to support users to search for imaes havin desired keywords instead of just basin on visual features as the previous query model. In case of bein unable to use keywords to query for users not knowin the lanuae of ocr

6 keywords (tourists not knowin the local lanuae), or not supportin devices, we allow users to input imaes containin queried keywords. Two search modes are supported: exact substrin matchin and approximate substrin matchin. Exact substrin matchin returns all imaes with substrins in the reconized text that are identical to the search strin. Approximate substrin matchin tolerates a certain number of character differences between the search strin and the reconized text. For approximate substrin matchin, we use the Levenshtein distance between the search strin and the text strin in imaes. For each imae, we calculate the minimal Levenshtein distance. If the minimal distance is below a certain threshold, the appearance of the strin in the imae is assumed. Fi. 8. Some example results of text detection on the ICDAR 2003 dataset V. EXPERIMENTAL RESULTS A. Text detection and reconition We preform our experiments on ICDAR 2003 text locatin competition dataset [15] which contains TrialTrain and TrialTest sets. This dataset contains imaes with various resolutions from to pixels, taken both indoor and outdoor. All the text strins in this dataset are in horizontal. The samples to train SVM classifier were collected from 251 TrialTrain set imaes. All 249 TrialTest set imaes were used to evaluate the proposed system. The standard definitions of word precision and recall defined in ICDAR 2003 competition were used [15]. TABLE I. PERFORMANCE COMPARISON OF TEXT DETECTION ALGORITHMS ON ICDAR 2003 DATASET. Method Precision Recall f Kim s method [17] Proposed method Epshtein [10] Yi [17] TH-TextLoc [17] Hinnerk Becker [16] Neumann [17] We show the text detection performance on the dataset in TABLE I. The method achieves a recall score similar to the winner of ICDAR 2011 Robust Readin competition [17], but the precision is worse than the ICDAR 2011 winner, which had not been published. However, the proposed method sinificantly outperforms the second best method of Yi [17] in all three measures. In comparison to previous text detection approaches, our alorithm offers the followin major advantaes. First, the pre-processin step based on morpholoical reconstruction helps to exclude substantial none-text objects as well as hihlihts text ones. Second, HOG feature can capture the texture and structure characteristics of text reions so it is suitable to text detection problem. Further, our system provides a reliable binarization for the detected text. Finally, the proposed alorithm is simple and efficient. Some example results of text detection on the ICDAR 2003 dataset are presented in Fi. 8. Fi. 9. depicts some examples that our method cannot handle to locate the text information. (a) (b) (c) Fi. 9. Examples of imaes where our method fails: (a) too small size, (b) less than 3 characters, (c) stron hihlihts We also evaluated the text reconition preformance on TrialTest set. The performance of the text reconition step is evaluated by two ratio measurements. Precision is the ratio of the number of the words correctly reconized to the total number of the words reconized. Recall is the ratio of the number of the words correctly reconized to the total number of the words detected from the detection stae. TABLE II. shows the reconition performance. Fi. 10. shows examples of text detection and reconition, where the left column is detection result, the middle column is the binarization result and the riht column is the reconition result. Experimental results show that the proposed binarization procedure is quite usable. TABLE II. RECOGNITION PERFORMANCE ON THE WORDS DETECTED IN ICDAR 2003 DATASET. Method Precision Recall Proposed method The heart of a fool is in his mouth but the mouth of a wise man is lin his heart (Benjamin Franklin) University of Essex nay Nursery The Houses Keynes, Rayleih Tawney and William Morris Towers Wolfson Canon Buses and Cycles Only CONFERENCE CAR PARK Edifice 4 Detected text Binarization results Reconized text Fi. 10. Binarization results and reconition results by the proposed methods B. Retrieval effectiveness In order to evaluate the efficiency of query by text, we use 50 words appearin in imae database as query keywords. In order to evaluate the efficiency of query by imaes, we use 25

7 imaes excluded in the database as query imae. We use two measures for the evaluation of retrieval effectiveness: precision and recall. Precision specifies the ratio of the number of relevant retrieval results to the total number of returned imaes. Recall specifies the ratio of the number of relevant results to the total number of relevant imaes in the imae database. We assume that an imae depictin the search text is retrieval correctly if at least one word in the search text appears in that imae. The result of query by imaes is shown in Fi. 11. The efficiency of query by keywords and query by imaes in exact substrin matchin is shown in TABLE III. Imae retrieval model based on scene text has not been proposed and published in any research so far, so we cannot compare the efficiency of the proposed model with other models. Experimental results show that our proposed text detection and reconition alorithms can be effectively used to retrieve relevant imaes. It shows the encourain results in queryin imae only usin scene text in imaes. VI. CONCLUSION In this paper, we have presented an imae retrieval method based on scene text. The scene text in imaes is detected and extracted by the proposed method which is robust aainst various conditions such as different font style, size, color, and scale of text. The binarized text can be directly used for text reconition purposes. The reconized words are used for indexin and retrieval. Experimental results have shown the proposed text detection method is quite competition to the other best existin methods. Our experimental results also proved that the proposed alorithms are suitable for indexin and retrieval of relevant imaes from an imae database. In future, we will evaluate our system on a lare database of imaes. We also attempt to improve the efficiency and incorporate the alorithms into existin content-based imae retrieval systems. Fi. 11. Retrieval result by imaes TABLE III. IMAGE RETRIEVAL PERFORMANCE OF THE PROPOSED MODEL IN EXACT SUBSTRING MATCHING Precision Recall Search by keywords Search by imaes REFERENCES [1] K. Jun, K. I. Kim, and A. K. Jain, Text in formation extraction in imaes and video: a survey, Pattern Reconition, vol. 37, no. 5, pp , [2] D. T. Chen, J. M. Odobez, and H. Bourland, Text detection and reconition in imaes and video frames, Pattern Reconition, vol. 37, no. 3, pp , [3] Q. Ye, W. Gao, W. Wan, W. Zen, A robust text detection alorithm in imae sand video frames, Joint Conference of Fourth International Conference on Information Communications and Sinal Processin and Pacific-Rim Conference on Multimedia, Sinapore [4] X. Chen and A. L. Yuille, Detectin and readin text in natural scenes, in CVPR, vol. 2, pp. II 366 II 373, [5] S. M. Hanif, L. Prevost, Text detection and localization in complex scene imaes usin constrained AdaBoost alorithm, in ICDAR, pp. 1-5, [6] Q. Ye, Q. Huan, W. Gao, and D. Zhao, Fast and robust text detection in imaes and video frames, Imae Vision Comput., vol. 23, pp , [7] Z. Liu and S. Sarkar, Robust outdoor text detection usin text intensity and shape features, in ICPR, pp. 1 4, [8] N. Ezaki, M. Bulacu, L. Schomaker, Text Detection from Natural Scene Imaes: Towards a System for Visually Impaired Persons, in ICPR, vol. 2, pp , [9] R. Datta, D. Joshi, J. Li, and J. Z. Wan, Imae retrieval: Ideas, influences, and trends of the new ae, ACM Computin Surveys, 40(2), [10] B. Epshtein, E. Ofek, and Y. Wexler, Detectin Text in Nature Scenes with Stroke Width Transform, in CVPR, pp , [11] P. Soille, Morpholoical Imae Analysis: Principles and Applications, pp , Spriner Verla, Berlin [12] K. Robinson, and P. F. Whelan, Efficient Morpholoical Reconstruction: A Downhill Filter, Pattern Reconition Letters, Volume 25, Issue 15, pp , [13] V. N. Vapnik, The Nature of Statistical Learnin Theory, Spriner, [14] S. Mihov, S. Koeva, C. Rinlstetter, K. U. Schulz and C. Strohmaier, Precise and Efficient Text Correction usin Levenshtein Automata, Dynamic Web Dictionaries and Optimized Correction Models, Proceedins of Workshop on International Proofin Tools and Lanuae Technoloies, [15] S. M. Lucas, A. Panaretos, L. Sosa, A. Tan, S. Won and R. Youn, ICDAR 2003 Robust Readin Competitions, in ICDAR, pp , [16] S. M. Lucas, ICDAR 2005 text locatin competition results, in ICDAR, vol. 1, pp , [17] A. Shahab, F. Shafait, and A. Denel, ICDAR 2011 robust readin competition challene 2: Readin text in scene imaes, in ICDAR, pp , 2011.