ICDAR 2013 Robust Reading Competition
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1 ICDAR 2013 Robust Reading Competition D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L. Gomez, S. Robles, J. Mas, D. Fernandez, J. Almazán, L.P. de las Heras
2 ICDAR 2013 Robust Reading Competition Challenge 1: Reading Text in Born-Digital Images (Web and ) Challenge 2: Reading Text in Scene Images Challenge 3: Reading Text in Videos 2
3 Timeline Web Site Online Registrations Open 15/1 Training Set Online 28/2 Test Set Available and Submission Open 29/3 Submission Closed 8/4 January February March April Open mode participation: Participants run their own algorithms and provided results. 3
4 Participation in Numbers Visits of the Web Site (Unique Visitors) 2,724 (1,025) Registered Users 124 Total Number of Individual Participants 15 Total Number of Submissions 52 (42 excluding variants) Challenge 1 #Submissions (#Participants) 17 (8) Challenge 2 #Submissions (#Participants) 22 (13) Challenge 3 #Submissions (#Participants) 1 (1) 4
5 Reading text in Born-Digital Images Reading text in Scene Images CHALLENGES 1 AND 2 5
6 Overview Born Digital Images Low-Resolution Digitally Created Text Compression Anti-Aliasing Real Scene Images High-Resolution Captured Text Illumination Artefacts Perspective 6
7 Structure Challenges 1 and 2 were organised over three tasks: Task 1 Text Localization Objective: To obtain a rough estimation of text areas in the image, in terms of bounding boxes corresponding to parts of text (words or text lines) Task 2 Text Segmentation Objective: Pixel-level separation of text from the background Task 3 Word Recognition Objective: Assuming known word bounding boxes, to obtain the correct text transcriptions 7
8 Datasets Updated datasets compared to the 2011 edition: Extra test images in Challenge 1 Segmentation ground truth and filtering of duplicate images in Challenge 2 Don t Care regions introduced in the ground truth Datasets in Numbers Challenge 1 Challenge 2 Training Dataset (Full Images) Test Dataset (Full Images) Training Dataset (Word Images) Test Dataset (Word Images)
![Ground Truth Task 1 Text Localization Dataset Images Ground Truth (text files) Ground Truth Visualisation 11, 15, 42, 28, "How" 41, 16, 61, 28, "to" 8, 33, 36, 46, "Find" 41, 32, 64, 46, "the" 11,](/docs-images/94/121046872/images/9-5.jpg "50, 61, 65, \"Perfect\" 16, 69, 56, 82, \"HDTV\" 22 249 113 286 \"The\" 142 249 287 286 \"Photo\" 326 245 620 297 \"Specialists\" 0, 1, 177, 32, \"\\"HIGHER\" 11, 35, 182, 63, \"SAVINGS\" 12, 67, 183, 103,")
9 Ground Truth Task 1 Text Localization Dataset Images Ground Truth (text files) Ground Truth Visualisation 11, 15, 42, 28, "How" 41, 16, 61, 28, "to" 8, 33, 36, 46, "Find" 41, 32, 64, 46, "the" 11, 50, 61, 65, "Perfect" 16, 69, 56, 82, "HDTV" "The" "Photo" "Specialists" 0, 1, 177, 32, "\"HIGHER" 11, 35, 182, 63, "SAVINGS" 12, 67, 183, 103, "RATES\"" 50, 114, 56, 120, "A" 60, 114, 91, 120, "reward" 96, 114, 108, 120, "for" 112, 116, 126, 120, "our" 130, 114, 164, 120, "current"... 9
10 Performance Evaluation Task 1 Text Localization Performance Evaluation Methodology Methodology proposed by Wolf and Jolion [1] Takes into account both Bounding Box area overlapping and Precision at the level of detection counts Possible to create meaningful cumulative results over many images Ways to deal with one-to-many and many-to-one cases Set up to penalise over-segmentation (words split to parts), but no undersegmentation (group of words detected as text line) Baseline Method Commercial OCR package (ABBYY OCR SDK v.10) 1. C. Wolf and J.M. Jolion, Object Count / Area Graphs for the Evaluation of Object Detection and Segmentation Algorithms, IJDAR 8(4), pp ,
11 Precision Results: Task 1.1 Text Localization % Method Name Recall Precision F-Score USTB_TexStar 82.38% 93.83% 87.73% TH-TextLoc 75.85% 86.82% 80.97% I2R_NUS_FAR 71.42% 84.17% 77.27% Baseline 69.21% 84.94% 76.27% Text Detection 73.18% 78.62% 75.80% I2R_NUS 67.52% 85.19% 75.33% BDTD_CASIA 67.05% 78.98% 72.53% OTCYMIST 74.85% 67.69% 71.09% Inkam 52.21% 58.12% 55.01% 90.00% 80.00% 70.00% 60.00% 50.00% 50.00% 60.00% 70.00% 80.00% 90.00% % Recall USTB_TexStar TH-TextLoc I2R_NUS_FAR Baseline Text Detection I2R_NUS BDTD_CASIA OTCYMIST Inkam Best ICDAR
12 Precision Results: Task 2.1 Text Localization % Method Name Recall Precision F-Score USTB_TexStar 66.45% 88.47% 75.90% Text Spotter 64.84% 87.51% 74.49% CASIA_NLPR 68.24% 78.89% 73.18% Text_Detector_CASIA 62.85% 84.70% 72.16% I2R_NUS_FAR 69.00% 75.08% 71.91% I2R_NUS 66.17% 72.54% 69.21% TH-TextLoc 65.19% 69.96% 67.49% Text Detection 53.42% 74.15% 62.10% Baseline 34.74% 60.76% 44.21% Inkam 35.27% 31.20% 33.11% 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% USTB_TexStar Recall Text Spotter CASIA_NLPR Text_Detector_CASIA I2R_NUS_FAR I2R_NUS TH-TextLoc Text Detection Baseline Inkam 12
13 Ground Truth Task 2 Text Segmentation Dataset Images Ground Truth (images) 13
![Performance Evaluation Task 2 Text Segmentation Performance Evaluation Methodology Framework proposed by Clavelli et al [2] Measures the](/docs-images/94/121046872/images/14-3.jpg "degree to which morphological properties of the text are preserved, not simply the number of misclassified pixels As a secondary")
14 Performance Evaluation Task 2 Text Segmentation Performance Evaluation Methodology Framework proposed by Clavelli et al [2] Measures the degree to which morphological properties of the text are preserved, not simply the number of misclassified pixels As a secondary evaluation scheme we implemented standard pixel level precision and recall (compatibility with other results) (a) (b) (c) 2. A. Clavelli, D. Karatzas and J. Lladós A Framework for the Assessment of Text Extraction Algorithms on Complex Colour Images, DAS 2010, pp ,
15 Precision Results: Task 1.2 Text Segmentation Method Name Recall Precision F-Score USTB_FuStar 80.01% 86.20% 82.99% I2R_NUS 64.57% 73.44% 68.72% OTCYMIST 65.75% 71.65% 68.57% I2R_NUS_FAR 59.05% 80.04% 67.96% Text Detection 49.64% 69.46% 57.90% % 90.00% 80.00% 70.00% 60.00% USTB_FuStar I2R_NUS OTCYMIST I2R_NUS_FAR Text Detection 50.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% % Recall 15
16 Precision Results: Task 2.2 Text Segmentation % Method Name Recall Precision F-Score I2R_NUS_FAR 68.64% 80.59% 74.14% NSTextractor 63.38% 83.57% 72.09% USTB_FuStar 68.03% 72.46% 70.18% I2R_NUS 60.33% 76.62% 67.51% NSTsegmentator 68.00% 54.35% 60.41% Text Detection 62.03% 57.43% 59.64% OTCYMIST 41.79% 31.60% 35.99% 90.00% 80.00% 70.00% 60.00% 50.00% 50.00% 60.00% 70.00% 80.00% 90.00% % Recall I2R_NUS_FAR NSTextractor USTB_FuStar I2R_NUS NSTsegmentator Text Detection OTCYMIST 16
17 Ground Truth Task 3 Word Recognition Dataset Images Ground Truth (single text file for all the words) 1.png 2.png 3.png 4.png 5.png 6.png 7.png 8.png 9.png 10.png 11.png 12.png 13.png 14.png 15.png 16.png 17.png 18.png 19.png 20.png 1.png, "okcupid" 2.png, "How" 3.png, "to" 4.png, "Find" 5.png, "the" 6.png, "Perfect" 7.png, "HDTV" 8.png, "Creative" 9.png, "Printing" 10.png, "Community" 11.png, "KIDS:" 12.png, "LEARN" 13.png, "ENGLISH" 14.png, "ONLINE" 15.png, "\"HIGHER " 16.png, "Games," 17.png, "stories," 18.png, "and" 19.png, "more" 20.png, "RATES\"" 17
18 Performance Evaluation Task 3 Word Recognition Performance Evaluation Methodology Edit distance (normalised to the length of the ground truth transcription) Equal weights for deletions, additions, substitutions We also report statistics on correctly recognised words (case sensitive) Baseline method Commercial OCR package (ABBYY OCR SDK v.10) 18
19 Results: Task 1.3 Word Recognition Method Name Total Edit Distance Correctly Recognized Words (%) PhotoOCR MAPS PLT NESP Baseline
20 Results: Task 2.3 Word Recognition Method Name Total Edit Distance Correctly Recognized Words (%) PhotoOCR PicRead NESP PLT MAPS Feild s Method PIONEER Baseline TextSpotter
21 Reading Text in Videos CHALLENGE 3 21
22 Overview - Structure Challenge 3 was organised over a single task: Task 1 Text Localization Objective: The objective of this task is to obtain a localisation of the words in the video in terms of their affine bounding boxes. The task requires that words are both localised correctly in every frame and tracked correctly (assigned the same ID) over the video sequence. 22
23 Datasets New Datasets created for this challenge Video sequences collected in different countries (English, French, Spanish) Users were given seven different tasks (e.g. Driving, follow way-finding panels, browse products in a super market, etc) Four different cameras were used (head-mounted, mobile, hand-held 720p, tripod high-definition 1080p) Sequences ranging from 10 to 1 06 each Dataset in Numbers Number of Videos in Training Set 13 Number of Frames in Training Set 5,486 Number of Videos in Test Set 15 Number of Frames in Test Set 9,790 Total Video Length
24 Ground Truth Frame Image Ground Truth Visualisation Ground Truth (XMLfile) <?xml version="1.0" encoding="us-ascii"?> <Frames> <frame ID="1" /> <frame ID="2" /> <frame ID="3" />... <frame ID="434"> <object Transcription="Badia" ID="377001" Language="Catalan Quality="MODERATE"> <Point x="446" y="58" /> <Point x="447" y="69" /> <Point x="484" y="67" /> <Point x="484" y="55" /> </object> <object Transcription="Barbera" ID="377002" Language="Catalan Quality="MODERATE"> <Point x="502" y="81" /> <Point x="447" y="85" /> <Point x="446" y="73" /> <Point x="502" y="70" /> </object>... </frame>... <frame ID="1859" /> <frame ID="1860" /> </Frames> 24
25 Performance Evaluation Evaluation based on CLEAR-MOT [3, 4], and VACE [5] metrics Multiple Object Tracking Precision (MOTP), expresses how well locations of words are estimated Multiple Object Tracking Accuracy (MOTA), how many mistakes the tracker has made (false positives, ID mismatches, etc), can be negative Average Tracking Accuracy (ATA), comprehensive spatio-temporal measure. Baseline Method Detection stage performed using the ABBYY OCR SDK, followed by a tracking stage where each detected word is assigned the identifier of the previously detected word with the best overlapping ratio 3. K. Bernardin and R. Stiefelhagen. Evaluating multiple object tracking performance: the CLEAR MOT metrics J. Image Video Process. January A.D. Bagdanov, A. Del Bimbo, F. Dini, G. Lisanti, and I. Masi, "Compact and efficient posterity logging of face imagery for video surveillance", IEEE Multimedia, R. Kasturi, et al, Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp ,
26 Results: Task 3.1 Text Localization Method Name MOTP MOTA ATA TextSpotter Baseline Baseline TextSpotter 26
27 27 WHAT S NEXT?
28 What s Next The competition site is open! Register to download the datasets and upload new results The competition is now open on a continuous mode Datasets are freely available (on Competition Web site and soon on TC11) Online performance evaluation functionality Ranking charts and tables Visualisation of the results of all tasks, and all challenges 28
29 Online Evaluation and Ranking Tables 29
30 Visualisation of different Tasks 30
31 Winners Thanks for participating! Task 1.1 Text Localization in Born Digital Images USTB_TexStar, Xuwang Yin 1, Xu-Cheng Yin 1 and Hong-Wei Hao 2 1 University of Science and Technology, Beijing, China 2 Institute of Automation, Chinese Academy of Sciences, Beijing, China Task 1.2 Text Segmentation in Born Digital Images USTB_FuStar, Xuwang Yin 1, Xu-Cheng Yin 1 and Hong-Wei Hao 2 1 University of Science and Technology, Beijing, China 2 Institute of Automation, Chinese Academy of Sciences, Beijing, China Task 1.3 Word Recognition in Born Digital Images Photo OCR, Alessandro Bissacco, Mark Cummins, Yuval Netzer, Hartmut Neven Google Inc., USA Task 2.1 Text Localization in Real Scenes USTB_TexStar, Xuwang Yin 1, Xu-Cheng Yin 1 and Hong-Wei Hao 2 1 University of Science and Technology, Beijing, China 2 Institute of Automation, Chinese Academy of Sciences, Beijing, China Task 2.2 Text Segmentation in Real Scenes I2R_NUS_FAR, Lu Shijian 1, Tian Shangxuan 2, Lim Joo Hwee 1, Tan Chew Lim 2 1 Institute for Infocomm Research, A*STAR, Singapore 2 School of Computing, National University of Singapore Task 2.3 Word Recognition in Real Scenes PhotoOCR, Alessandro Bissacco, Mark Cummins, Yuval Netzer, Hartmut Neven Google Inc., USA Task 3.1 Text Localization in Videos (Mention) TextSpotter, Lukas Neumann, Jiri Matas, Michal Busta Czech Technical University, Czech Republic 31
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