25.4.6.3.1 Cursive Script, Word Level Recognition, Word Spotting, Language Model

Chapter Contents (Back)
Cursive Character Recognition. Handwriting. Word Recognition. Word Level Recognition. Word Spotting. For more related to word level analysis in printed documents:
See also Word Level Recognition, Language Models.
See also Arabic Recognition, Word Level, Word Spotting.

Sayre, K.M.[Kenneth M.],
Machine Recognition of Handwritten Words: A Project Report,
PR(5), No. 3, September 1973, pp. 213-228.
Elsevier DOI BibRef 7309

Farag, R.F.H.,
Word-Level Recognition of Cursive Script,
TC(28), No. 2, February 1979, pp. 172-175. BibRef 7902

Hayes, K.C.,
Reading Handwritten Words Using Hierarchical Relaxation,
CGIP(14), No. 4, December 1980, pp. 344-364.
Elsevier DOI BibRef 8012
And:
Understanding Handwriting,
UMD-TR, 1981. BibRef Ph.D.Thesis. BibRef

Brown, M.K., and Ganapathy, S.,
Preprocessing Techniques for Cursive Script Word Recognition,
PR(16), No. 5, 1983, pp. 447-458.
Elsevier DOI Skew Correction. Skew correction. First skew then slant. BibRef 8300

Kundu, A.[Amlan], He, Y.[Yang], Bahl, P.[Paramvir],
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Elsevier DOI 0309
BibRef
Earlier: CVPR88(457-462).
IEEE DOI BibRef

Kundu, A., Beach, R.,
Machine reading of handwritten text information in field technician's maps,
ICIP98(II: 943-947).
IEEE DOI 9810
BibRef

Simon, J.C.,
Off-Line Cursive Word Recognition,
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IEEE Top Reference. BibRef 9207

Paquet, T.[Thierry], Lecourtier, Y.[Yves],
Recognition of handwritten sentences using a restricted lexicon,
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Chen, M.Y.[Mou-Yen], Kundu, A.[Amlan], and Zhou, J.,
Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network,
PAMI(16), No. 5, May 1994, pp. 481-496.
IEEE DOI BibRef 9405
Earlier:
Off-line handwritten word recognition (HWR) using a single contextual hidden Markov model,
CVPR92(669-672).
IEEE DOI 0403
BibRef

He, Y.[Yang], Chen, M.Y.[Mou-Yen], Kundu, A.,
Off-line handwritten word recognition using HMM with adaptive length Viterbi algorithm,
ICPR94(B:460-462).
IEEE DOI 9410
BibRef

Chen, M.Y.[Mou-Yen], Kundu, A.[Amlan], and Srihari, S.N.,
Variable Duration Hidden Markov Model and Morphological Segmentation for Handwritten Word Recognition,
IP(4), No. 12, December 1995, pp. 1675-1688.
IEEE DOI BibRef 9512
Earlier: CVPR93(600-601).
IEEE DOI BibRef

Chen, M.Y.[Mou-Yen], Kundu, A.,
A complement to variable duration hidden Markov model in handwritten word recognition,
ICIP94(I: 174-178).
IEEE DOI 9411
BibRef

Kundu, A.[Amlan], He, Y.[Yang], Chen, M.Y.[Mou-Yen],
Alternatives to Variable Duration HMM in Handwriting Recognition,
PAMI(20), No. 11, November 1998, pp. 1275-1280.
IEEE DOI 9811
BibRef

Kundu, A., Hines, T., Huyck, B., Phillips, J., van Guilder, L.,
Arabic Handwriting Recognition Using Variable Duration HMM,
ICDAR07(644-648).
IEEE DOI 0709
BibRef

Gilloux, M., Leroux, M., Bertille, J.M.,
Strategies for Cursive Script Recognition Using Hidden Markov-Models,
MVA(8), No. 4, 1995, pp. 197-205.
Springer DOI BibRef 9500

Gilloux, M.,
Writer adaptation for handwritten word recognition using hidden Markov models,
ICPR94(B:135-139).
IEEE DOI 9410
BibRef

Leroux, M., Salome, J.C., and Badard, J.,
Recognition of Cursive Script Words in a Small Lexicon,
ICDAR91(774-xx). BibRef 9100

El-Yacoubi, M.A.[Mounim A.], Gilloux, M.[Michel], Sabourin, R., Suen, C.Y.,
An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition,
PAMI(21), No. 8, August 1999, pp. 752-760.
IEEE DOI Generate models of the written words -- shape, segmentation, shape, seg... BibRef 9908

Koerich, A.L., Sabourin, R.[Robert], Suen, C.Y.,
Recognition and Verification of Unconstrained Handwritten Words,
PAMI(27), No. 10, October 2005, pp. 1509-1522.
IEEE DOI 0509
Verify words from a large vocabulary. Start with segments then combine for words probabilities. BibRef

Koerich, A.L.[Alessandro L.], Sabourin, R.[Robert], Suen, C.Y.[Ching Y.],
Lexicon-driven HMM decoding for large vocabulary handwriting recognition with multiple character models,
IJDAR(6), No. 2, 2003, pp. 126-144.
Springer DOI 0310
BibRef
Earlier:
A distributed scheme for lexicon-driven handwritten word recognition and its application to large vocabulary problems,
ICDAR01(660-664).
IEEE DOI 0109
BibRef

Koerich, A.L., Leydier, Y., Sabourin, R., Suen, C.Y.,
A hybrid large vocabulary handwritten word recognition system using neural networks with hidden markov models,
FHR02(99-104).
IEEE Top Reference. 0209
BibRef

Suen, C.Y.[Ching Y.], Sabourin, R.[Robert], El-Yacoubi, A.[Abdenaim],
Improved Model Architecture and Training Phase in an Off-Line HMM-Based Word Recognition System,
ICPR98(Vol II: 1521-1525).
IEEE DOI 9808
BibRef

Morita, M., Sabourin, R., Bortolozzi, F., Suen, C.Y.,
Unsupervised Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Word Recognition,
ICDAR03(666-670).
IEEE DOI 0311

See also Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy. BibRef

Clergeautournemire, S., Plamondon, R.,
Integration of Lexical and Syntactic Knowledge in a Handwriting-Recognition System,
MVA(8), No. 4, 1995, pp. 249-259.
Springer DOI BibRef 9500

Kim, G.H.[Gyeong-Hwan], Govindaraju, V.[Venu],
A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications,
PAMI(19), No. 4, April 1997, pp. 366-379.
IEEE DOI 9705
Preprocessing is done on a chain code representing the word contour. Recognition on a lexicon of 10 words. BibRef

Kim, G.H.[Gyeong-Hwan], Govindaraju, V.[Venu],
Handwritten Phrase Recognition As Applied To Street Name Images,
PR(31), No. 1, January 1998, pp. 41-51.
Elsevier DOI 9802
BibRef
Earlier:
Recognition of Handwritten Phrases as Applied to Street Name Images,
CVPR96(459-464).
IEEE DOI BibRef
And:
Efficient Chain Code Based Image Manipulation for Handwritten Word Recognition,
SPIE(2660), 1996, pp. 262-272. BibRef

Govindaraju, V., Krishnamurthy, R.K.,
Holistic Handwritten Word Recognition Using Temporal Features Derived from Off-Line Images,
PRL(17), No. 5, May 1 1996, pp. 537-540. 9606
BibRef

Madhvanath, S.[Sriganesh], Kleinberg, E.[Evelyn], Govindaraju, V.[Venu],
Holistic Verification of Handwritten Phrases,
PAMI(21), No. 12, December 1999, pp. 1344-1356.
IEEE DOI 0001
Word verification, as for street names.
See also Local reference lines for handwritten phrase recognition. BibRef

Madhvanath, S., Kleinberg, E., Govindaraju, V., Srihari, S.N.,
The Hover System for Rapid Holistic Verification of Off-Line Handwritten Phrases,
ICDAR97(855-859).
IEEE DOI 9708
BibRef

Madhvanath, S., Govindaraju, V., Srihari, S.N.,
Reading Handwritten Phrases on US Census Forms,
IJIST(7), No. 4, Winter 1996, pp. 312-319. 9612
BibRef

Madhvanath, S., Kim, G.H.[Gyeong-Hwan], Govindaraju, V.,
Chaincode Contour Processing for Handwritten Word Recognition,
PAMI(21), No. 9, September 1999, pp. 928-932.
IEEE DOI Chain Codes. Processing exterior contour of the signatures. BibRef 9909

Madhvanath, S.[Sriganesh], Govindaraju, V.[Venu],
The Role of Holistic Paradigms in Handwritten Word Recognition,
PAMI(23), No. 2, February 2001, pp. 149-164.
IEEE DOI 0102
BibRef
Earlier:
Contour-Based Image Preprocessing for Holistic Handwritten Word Recognition,
ICDAR97(536-539).
IEEE DOI 9708
BibRef
And:
Pruning Large Lexicons Using Generalized Word Shape Descriptors,
ICDAR97(552-555).
IEEE DOI 9708
The word is the entity, recognize words.
See also Local reference lines for handwritten phrase recognition. BibRef

Madhvanath, S., Krpasundar, V., Govindaraju, V.,
Syntactic methodology of pruning large lexicons in cursive script recognition,
PR(34), No. 1, January 2001, pp. 37-46.
Elsevier DOI 0010
BibRef

Kim, G.H.[Gyeong-Hwan], Govindaraju, V., Srihari, S.N.,
A Segmentation and Recognition Strategy for Handwritten Phrases,
ICPR96(IV: 510-514).
IEEE DOI 9608
(State Univ. of NY-Buffalo, USA) BibRef

Dewaard, W.P.,
An Optimized Minimal Edit Distance for Hand-Written Word Recognition,
PRL(16), No. 10, October 1995, pp. 1091-1096. BibRef 9510

Eliaz, A., Geiger, D.,
Word-Level Recognition of Small Sets of Hand-Written Words,
PRL(16), No. 10, October 1995, pp. 999-1009. BibRef 9510

Cho, W.Y., Lee, S.W., Kim, J.H.,
Modeling and Recognition of Cursive Words with Hidden Markov-Models,
PR(28), No. 12, December 1995, pp. 1941-1953.
Elsevier DOI BibRef 9512

Gader, P.D., Mohamed, M.A., Keller, J.M.,
Dynamic-Programming-Based Handwritten Word Recognition Using the Choquet Fuzzy Integral as the Match Function,
JEI(5), No. 1, January 1996, pp. 15-24. BibRef 9601

Gader, P.D., Mohamed, M.A., Chinag, J.H.,
Comparison of Crisp and Fuzzy Character Neural Networks in Handwritten Word Recognition,
Fuzzy(3), No. 3, August 1995, pp. 357-364. BibRef 9508

Gader, P.D., Mohamed, M.A., Chiang, J.H.,
Handwritten Word Recognition with Character and Inter-Character Neural Networks,
SMC-B(27), No. 1, February 1997, pp. 158-164.
IEEE Top Reference. 9702
Compare to a given lexicon of words. BibRef

Mohamed, M.A., Gader, P.D.,
Handwritten Word Recognition Using Segmentation-Free Hidden Markov Modeling and Segmentation-Based Dynamic-Programming Techniques,
PAMI(18), No. 5, May 1996, pp. 548-554.
IEEE DOI 9606
Recognize words, not letters. BibRef

Gader, P.D., Whalen, M., Ganzberger, M., Hepp, D.,
Handprinted Word Recognition on a NIST Data Set,
MVA(8), No. 1, 1995, pp. 31-40.
Springer DOI Performance Analysis. BibRef 9500

Gader, P.D., Mohamed, M.A., Keller, J.M.,
Fusion of Handwritten Word Classifiers,
PRL(17), No. 6, May 15 1996, pp. 577-584. 9607
BibRef

Chiang, J.H.,
A Hybrid Neural Network Model in Handwritten Word Recognition,
NeurNet(11), No. 2, March 1998, pp. 337-346. 9806
BibRef

Parisse, C.,
Global Word Shape Processing in Off-Line Recognition of Handwriting,
PAMI(18), No. 4, April 1996, pp. 460-464.
IEEE DOI 9605
Large lexicon (16000 words). Recognition by words, not letter by letter. Uses profiles (fine and coarse). BibRef

Powalka, R.K., Sherkat, N., Whitrow, R.J.,
Word Shape-Analysis for a Hybrid Recognition System,
PR(30), No. 3, March 1997, pp. 421-445.
Elsevier DOI 9705
BibRef

Sherkat, N.,
Influence of zoning on whole word recognition,
ICDAR05(II: 1085-1089).
IEEE DOI 0508
BibRef

Saon, G.[George],
Cursive word recognition using a random field based hidden Markov model,
IJDAR(1), No. 4 1999, pp. 199-208. BibRef 9900

Zimmermann, M.[Matthias], Mao, J.[Jianchang],
Lexicon reduction using key characters in cursive handwritten words,
PRL(20), No. 11-13, November 1999, pp. 1297-1304. 0001
BibRef

Vinciarelli, A.[Alessandro], Luettin, J.[Juergen],
A new normalization technique for cursive handwritten words,
PRL(22), No. 9, July 2001, pp. 1043-1050.
Elsevier DOI 0106
BibRef

Verma, B., Gader, P.D., Chen, W.T.,
Fusion of multiple handwritten word recognition techniques,
PRL(22), No. 9, July 2001, pp. 991-998.
Elsevier DOI 0106
BibRef

Liu, J.H.[Jin-Hui], Gader, P.D.[Paul D.],
Neural networks with enhanced outlier rejection ability for off-line handwritten word recognition,
PR(35), No. 10, October 2002, pp. 2061-2071.
Elsevier DOI 0206
BibRef
Earlier:
Outlier Rejection with MLPs and Variants of RBF Networks,
ICPR00(Vol II: 680-683).
IEEE DOI 0009
BibRef

Marti, U.V., Bunke, H.,
The IAM-database: an English sentence database for offline handwriting recognition,
IJDAR(5), No. 1, 2002, pp. 39-46.
Springer DOI 0211
BibRef
Earlier:
On the influence of vocabulary size and language models in unconstrained handwritten text recognition,
ICDAR01(260-265).
IEEE DOI 0109
BibRef

Marti, U.V., Messerli, R., Bunke, H.,
Writer identification using text line based features,
ICDAR01(101-105).
IEEE DOI 0109
BibRef

Marti, U.V., Bunke, H.,
Text line segmentation and word recognition in a system for general writer independent handwriting recognition,
ICDAR01(159-163).
IEEE DOI 0109
BibRef

Marti, U.V., Bunke, H.,
Handwritten Sentence Recognition,
ICPR00(Vol III: 463-466).
IEEE DOI 0009
BibRef

Vinciarelli, A.[Alessandro],
A survey on off-line Cursive Word Recognition,
PR(35), No. 7, July 2002, pp. 1433-1446.
Elsevier DOI 0204
Survey, Handwriting. BibRef

Vinciarelli, A.[Alessandro], Bengio, S.[Samy],
Writer adaptation techniques in HMM based Off-Line Cursive Script Recognition,
PRL(23), No. 8, June 2002, pp. 905-916.
Elsevier DOI 0204
BibRef
Earlier:
Writer adaptation techniques in off-line cursive word recognition,
FHR02(287-291).
IEEE Top Reference. 0209
BibRef
Earlier:
Offline cursive word recognition using continuous density hidden Markov models trained with PCA or ICA features,
ICPR02(III: 81-84).
IEEE DOI 0211
BibRef

Vinciarelli, A.[Alessandro], Bengio, S.[Samy], Bunke, H.[Horst],
Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models,
PAMI(26), No. 6, June 2004, pp. 709-720.
IEEE Abstract. 0404
BibRef
Earlier:
Offline Recognition of Large Vocabulary Cursive Handwritten Text,
ICDAR03(1101-1105).
IEEE DOI 0311
Use language models to improve recognition of cursive script. BibRef

Zimmermann, M., Bunke, H.,
Optimizing the integration of a statistical language model in HMM based offline handwritten text recognition,
ICPR04(II: 541-544).
IEEE DOI 0409
BibRef

Bertolami, R.[Roman], Zimmermann, M.[Matthias], Bunke, H.[Horst],
Rejection Strategies for Offline Handwritten Text Line Recognition,
PRL(27), No. 16, December 2006, pp. 2005-2012.
Elsevier DOI 0611
BibRef
Earlier: A2, A1, A3:
Rejection strategies for offline handwritten sentence recognition,
ICPR04(II: 550-553).
IEEE DOI 0409
Handwritten text recognition; Rejection strategies; Statistical language model BibRef

Bertolami, R.[Roman], Bunke, H.[Horst],
Hidden Markov Model-Based Ensemble Methods for Offline Handwritten Text Line Recognition,
PR(41), No. 11, November 2008, pp. 3452-3460.
Elsevier DOI 0808
BibRef
Earlier:
Multiple Handwritten Text Line Recognition Systems Derived from Specific Integration of a Language Model,
ICDAR05(I: 521-527).
IEEE DOI 0508
Offline handwritten text line recognition; Ensemble methods; Confidence measures BibRef

Bunke, H., Roth, M., Schukat-Talamazzini, E.G.,
Off-Line Cursive Handwriting Recognition Using Hidden Markov-Models,
PR(28), No. 9, September 1995, pp. 1399-1413.
Elsevier DOI BibRef 9509
Earlier:
Off-line recognition of cursive script produced by a cooperative writer,
ICPR94(B:383-386).
IEEE DOI 9410
BibRef

Camastra, F.[Francesco], Vinciarelli, A.[Alessandro],
Cursive character recognition by learning vector quantization,
PRL(22), No. 6-7, May 2001, pp. 625-629.
Elsevier DOI 0105
BibRef

Camastra, F.[Francesco], Spinetti, M.[Marco], Vinciarelli, A.[Alessandro],
Offline Cursive Character Challenge: a New Benchmark for Machine Learning and Pattern Recognition Algorithms.,
ICPR06(II: 913-916).
IEEE DOI 0609
BibRef

Camastra, F.[Francesco],
A SVM-based cursive character recognizer,
PR(40), No. 12, December 2007, pp. 3721-3727.
Elsevier DOI 0709
Support vector machines; Neural gas; Learning vector quantization; Multi-layer-perceptron; Crossvalidation; Cursive character recognition BibRef

Pitrelli, J.F.[John F.], Roy, A.[Amit],
Creating word-level language models for large-vocabulary handwriting recognition,
IJDAR(5), No. 2-3, April 2003, pp. 126-137.
Springer DOI 0308
BibRef
Earlier:
Creating word-level language models for handwriting recognition,
ICDAR01(721-725).
IEEE DOI 0109
BibRef

Pitrelli, J.F.[John F.], Subrahmonia, J.[Jayashree], Perrone, M.P.[Michael P.],
Confidence modeling for handwriting recognition: Algorithms and Applications,
IJDAR(8), No. 1, April 2006, pp. 35-46.
Springer DOI 0605
BibRef

Pitrelli, J.F., Perrone, M.P.,
Confidence-scoring post-processing for off-line handwritten-character recognition verification,
ICDAR03(278-282).
IEEE DOI 0311
BibRef
Earlier:
Confidence modeling for verification post-processing for handwriting recognition,
FHR02(30-35).
IEEE Top Reference. 0209
BibRef

Günter, S.[Simon], Bunke, H.[Horst],
HMM-based handwritten word recognition: on the optimization of the number of states, training iterations and Gaussian components,
PR(37), No. 10, October 2004, pp. 2069-2079.
Elsevier DOI 0409
BibRef
Earlier:
Optimizing the number of states, training iterations and Gaussians in an HMM-based handwritten word recognizer,
ICDAR03(472-476).
IEEE DOI 0311
BibRef

Graves, A.[Alex], Liwicki, M.[Marcus], Fernández, S.[Santiago], Bertolami, R.[Roman], Bunke, H.[Horst], Schmidhuber, J.[Jürgen],
A Novel Connectionist System for Unconstrained Handwriting Recognition,
PAMI(31), No. 5, May 2009, pp. 855-868.
IEEE DOI 0903
Issues of segmentation and context. NN for sequence labelling without segmentation. BibRef

Indermuhle, E.[Emanuel], Frinken, V.[Volkmar], Bunke, H.[Horst],
Mode Detection in Online Handwritten Documents Using BLSTM Neural Networks,
FHR12(302-307).
IEEE DOI 1302
BibRef

Indermuhle, E.[Emanuel], Frinken, V.[Volkmar], Fischer, A.[Andreas], Bunke, H.[Horst],
Keyword Spotting in Online Handwritten Documents Containing Text and Non-text Using BLSTM Neural Networks,
ICDAR11(73-77).
IEEE DOI 1111
BibRef

Frinken, V.[Volkmar], Fischer, A.[Andreas], Manmatha, R., Bunke, H.[Horst],
A Novel Word Spotting Method Based on Recurrent Neural Networks,
PAMI(34), No. 2, February 2012, pp. 211-224.
IEEE DOI 1112
Derived from NN approach for handwriting recognition. Not necessary for keyword to appear in training set. BibRef

Liwicki, M., Indermuhle, E., Bunke, H.,
On-Line Handwritten Text Line Detection Using Dynamic Programming,
ICDAR07(447-451).
IEEE DOI 0709
BibRef

Liwicki, M.[Marcus], Scherz, M.[Mathias], Bunke, H.[Horst],
Word Extraction from On-Line Handwritten Text Lines,
ICPR06(II: 929-933).
IEEE DOI 0609
BibRef

Frinken, V.[Volkmar], Bunke, H.[Horst],
Self-training for Handwritten Text Line Recognition,
CIARP10(104-112).
Springer DOI 1011
BibRef

Varga, T., Bunke, H.,
Tree structure for word extraction from handwritten text lines,
ICDAR05(I: 352-356).
IEEE DOI 0508
BibRef
Earlier:
Off-line handwritten textline recognition using a mixture of natural and synthetic training data,
ICPR04(II: 545-549).
IEEE DOI 0409
BibRef
Earlier:
Generation of synthetic training data for an HMM-based handwriting recognition system,
ICDAR03(618-622).
IEEE DOI 0311
BibRef

Günter, S.[Simon], Bunke, H.[Horst],
Feature selection algorithms for the generation of multiple classifier systems and their application to handwritten word recognition,
PRL(25), No. 11, August 2004, pp. 1323-1336.
Elsevier DOI 0409
BibRef

Zimmermann, M., Chappelier, J.C., Bunke, H.,
Offline Grammar-Based Recognition of Handwritten Sentences,
PAMI(28), No. 5, May 2006, pp. 818-821.
IEEE DOI 0604
BibRef
Earlier:
Parsing N-best lists of handwritten sentences,
ICDAR03(572-576).
IEEE DOI 0311
BibRef

Rath, T.M.[Toni M.], Manmatha, R.,
Word spotting for historical documents,
IJDAR(9), No. 2-4, April 2007, pp. 139-152.
Springer DOI 0704
BibRef
And: IJDAR(9), No. 2-4, April 2007, pp. 299.
Springer DOI 0704
BibRef
Earlier:
Word image matching using dynamic time warping,
CVPR03(II: 521-527).
IEEE DOI 0307
BibRef
And:
Features for word spotting in historical manuscripts,
ICDAR03(218-222).
IEEE DOI 0311
BibRef

Lavrenko, V., Rath, T.M.[Toni M.], Manmatha, R.,
Holistic word recognition for handwritten historical documents,
DIAL04(278-287).
IEEE DOI 0404
BibRef

Adamek, T.[Tomasz], O'Connor, N.E.[Noel E.], Smeaton, A.F.[Alan F.],
Word matching using single closed contours for indexing handwritten historical documents,
IJDAR(9), No. 2-4, April 2007, pp. 153-165.
Springer DOI 0704

See also multiscale representation method for nonrigid shapes with a single closed contour, A. BibRef

Konidaris, T., Gatos, B., Ntzios, K., Pratikakis, I.E., Theodoridis, S., Perantonis, S.J.,
Keyword-guided word spotting in historical printed documents using synthetic data and user feedback,
IJDAR(9), No. 2-4, April 2007, pp. 167-177.
Springer DOI 0704

See also Handwritten character recognition through two-stage foreground sub-sampling. BibRef

Gatos, B.[Basilis], Pratikakis, I.E.[Ioannis E.],
Segmentation-Free Word Spotting in Historical Printed Documents,
ICDAR09(271-275).
IEEE DOI 0907
BibRef

Ruiz-Pinales, J.[Jose], Jaime-Rivas, R.[Rene], Castro-Bleda, M.J.[Maria Jose],
Holistic cursive word recognition based on perceptual features,
PRL(28), No. 13, 1 October 2007, pp. 1600-1609.
Elsevier DOI 0709
Holistic cursive script recognition; Perceptual features; Convolutional feature extraction BibRef

Farooq, F.[Faisal], Jose, D.[Damien], Govindaraju, V.[Venu],
Phrase-based correction model for improving handwriting recognition accuracies,
PR(42), No. 12, December 2009, pp. 3271-3277.
Elsevier DOI 0909
Post-processing; Noisy channel; Handwriting recognition; Error correction; Viterbi decoding BibRef

Howe, N.R.[Nicholas R.], Feng, S.L.[Shao-Lei], Manmatha, R.,
Finding words in alphabet soup: Inference on freeform character recognition for historical scripts,
PR(42), No. 12, December 2009, pp. 3338-3347.
Elsevier DOI 0909
Character recognition; Cursive text; Historical text BibRef

Cao, H.G.[Huai-Gu], Bhardwaj, A.[Anurag], Govindaraju, V.[Venu],
A probabilistic method for keyword retrieval in handwritten document images,
PR(42), No. 12, December 2009, pp. 3374-3382.
Elsevier DOI 0909
Word spotting; Information retrieval; Handwriting recognition BibRef

Cao, H.G.[Huai-Gu], Govindaraju, V.[Venu], Bhardwaj, A.[Anurag],
Unconstrained handwritten document retrieval,
IJDAR(14), No. 2, June 2011, pp. 145-157.
WWW Link. 1106

See also Automatic recognition of handwritten medical forms for search engines. BibRef

Li, Q.[Qing], Chen, Y.Z.P.[Yuan-Zhu Peter],
Personalized text snippet extraction using statistical language models,
PR(43), No. 1, January 2010, pp. 378-386.
Elsevier DOI 0909
Text snippet extraction; Personalization; Language model; Information retrieval; Natural language processing; Pattern discovery; Hidden Markov Model BibRef

Farooq, F.[Faisal], Bhardwaj, A.[Anurag], Govindaraju, V.[Venu],
Using topic models for OCR correction,
IJDAR(12), No. 3, September 2009, pp. xx-yy.
Springer DOI 0911
BibRef

Can, E.F.[Ethem Fatih], Duygulu, P.[Pinar],
A line-based representation for matching words in historical manuscripts,
PRL(32), No. 8, 1 June 2011, pp. 1126-1138.
Elsevier DOI 1101
Historical manuscripts; Word image matching; Word retrieval; Word spotting; Line-based representation BibRef

Can, E.F.[Ethem Fatih], Duygulu, P.[Pinar], Can, F.[Fazli], Kalpakli, M.[Mehmet],
Redif Extraction in Handwritten Ottoman Literary Texts,
ICPR10(1941-1944).
IEEE DOI 1008
BibRef

Ataer, E.[Esra], Duygulu, P.[Pinar],
Matching Ottoman Words: An image retrieval approach to historical document indexing,
CIVR07(341-347).
DOI Link 0707
BibRef

Ataer, E.[Esra], Duygulu, P.[Pinar],
Retrieval of Ottoman documents,
MIR06(155-162).
DOI Link 0707
BibRef

Bianne-Bernard, A.L.[Anne-Laure], Menasri, F.[Fares], Mohamad, R.A.H.[Rami Al-Hajj], Mokbel, C.[Chafic], Kermorvant, C.[Christopher], Likforman-Sulem, L.[Laurence],
Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition,
PAMI(33), No. 10, October 2011, pp. 2066-2080.
IEEE DOI 1109
Word recognition by combining 3 separate handwritting techniques. Contextual modeling and dynamic modeling improves recognition. BibRef

Kermorvant, C., Menasri, F., Bianne-Bernard, A.L.[Anne-Laure], Mohamad, R.A.H.[Rami Al-Hajj], Mokbel, C., Likforman-Sulem, L.,
The A2iA-Telecom ParisTech-UOB System for the ICDAR 2009 Handwriting Recognition Competition,
FHR10(247-252).
IEEE DOI 1011
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Fischer, A.[Andreas], Keller, A.[Andreas], Frinken, V.[Volkmar], Bunke, H.[Horst],
Lexicon-free handwritten word spotting using character HMMs,
PRL(33), No. 7, 1 May 2012, pp. 934-942.
Elsevier DOI 1203
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HMM-based Word Spotting in Handwritten Documents Using Subword Models,
ICPR10(3416-3419).
IEEE DOI 1008
Award, ICPR. Handwriting recognition; Keyword spotting; Hidden Markov Models BibRef

Frinken, V.[Volkmar], Baumgartner, M.[Markus], Fischer, A.[Andreas], Bunke, H.[Horst],
Semi-supervised Learning for Cursive Handwriting Recognition Using Keyword Spotting,
FHR12(49-54).
IEEE DOI 1302
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Stauffer, M.[Michael], Fischer, A.[Andreas], Riesen, K.[Kaspar],
A Novel Graph Database for Handwritten Word Images,
SSSPR16(553-563).
Springer DOI 1611
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Scius-Bertrand, A.[Anna], Studer, L.[Linda], Fischer, A.[Andreas], Bui, M.[Marc],
Annotation-Free Keyword Spotting in Historical Vietnamese Manuscripts Using Graph Matching,
SSSPR22(22-32).
Springer DOI 2301
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Stauffer, M.[Michael], Fischer, A.[Andreas], Riesen, K.[Kaspar],
Speeding-Up Graph-Based Keyword Spotting by Quadtree Segmentations,
CAIP17(I: 304-315).
Springer DOI 1708
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Earlier:
Speeding-Up Graph-Based Keyword Spotting in Historical Handwritten Documents,
GbRPR17(83-93).
Springer DOI 1706
BibRef
Earlier:
Graph-Based Keyword Spotting in Historical Handwritten Documents,
SSSPR16(564-573).
Springer DOI 1611
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Riesen, K.[Kaspar], Brodic, D.[Darko], Milivojevic, Z.N.[Zoran N.], Maluckov, C.A.[Cedomir A.],
Graph Based Keyword Spotting in Medieval Slavic Documents: A Project Outline,
EuroMed14(724-731).
Springer DOI 1412
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Stauffer, M.[Michael], Fischer, A.[Andreas], Riesen, K.[Kaspar],
Keyword spotting in historical handwritten documents based on graph matching,
PR(81), 2018, pp. 240-253.
Elsevier DOI 1806
Handwritten keyword spotting, Graph representation, Bipartite graph matching, Ensemble methods BibRef

Ameri, M.R.[Mohammad Reza], Stauffer, M.[Michael], Riesen, K.[Kaspar], Bui, T.D.[Tien D.], Fischer, A.[Andreas],
Graph-based keyword spotting in historical manuscripts using Hausdorff edit distance,
PRL(121), 2019, pp. 61-67.
Elsevier DOI 1904
Keyword spotting, Handwriting graphs, Graph matching, Hausdorff edit distance BibRef

Stauffer, M.[Michael], Fischer, A.[Andreas], Riesen, K.[Kaspar],
Filters for graph-based keyword spotting in historical handwritten documents,
PRL(134), 2020, pp. 125-134.
Elsevier DOI 2005
Handwritten keyword spotting, Graph representation, Bipartite graph matching, Filter methods, Fast rejection BibRef

Frinken, V.[Volkmar], Fischer, A.[Andreas], Bunke, H.[Horst], Manmatha, R.,
Adapting BLSTM Neural Network Based Keyword Spotting Trained on Modern Data to Historical Documents,
FHR10(352-357).
IEEE DOI 1011
BibRef

Frinken, V.[Volkmar], Bunke, H.[Horst],
Evaluating Retraining Rules for Semi-Supervised Learning in Neural Network Based Cursive Word Recognition,
ICDAR09(31-35).
IEEE DOI 0907
BibRef

Fischer, A.[Andreas], Indermuhle, E.[Emanuel], Frinken, V.[Volkmar], Bunke, H.[Horst],
HMM-Based Alignment of Inaccurate Transcriptions for Historical Documents,
ICDAR11(53-57).
IEEE DOI 1111
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Frinken, V.[Volkmar], Fischer, A.[Andreas], Bunke, H.[Horst], Foornes, A.[Alicia],
Co-training for Handwritten Word Recognition,
ICDAR11(314-318).
IEEE DOI 1111
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Jain, R.[Raman], Frinken, V.[Volkmar], Jawahar, C.V., Manmatha, R.,
BLSTM Neural Network Based Word Retrieval for Hindi Documents,
ICDAR11(83-87).
IEEE DOI 1111
BibRef

Khurshid, K.[Khurram], Faure, C.[Claudie], Vincent, N.[Nicole],
Word spotting in historical printed documents using shape and sequence comparisons,
PR(45), No. 7, July 2012, pp. 2598-2609.
Elsevier DOI 1203
BibRef
Earlier:
Fusion of Word Spotting and Spatial Information for Figure Caption Retrieval in Historical Document Images,
ICDAR09(266-270).
IEEE DOI 0907
BibRef
Earlier:
A Novel Approach for Word Spotting Using Merge-Split Edit Distance,
CAIP09(213-220).
Springer DOI 0909
Word spotting; Historical documents; Digital libraries; Dynamic Time Warping; Segmentation-driven string matching; Information retrieval BibRef

Liang, Y., Fairhurst, M.C., Guest, R.M.,
A synthesised word approach to word retrieval in handwritten documents,
PR(45), No. 12, December 2012, pp. 4225-4236.
Elsevier DOI 1208
BibRef
And: A1, A3, A2:
Implementing Word Retrieval in Handwritten Documents Using a Small Dataset,
FHR12(728-733).
IEEE DOI 1302
Handwriting analysis; Digital archives; Handwritten word retrieval; Word spotting; Information retrieval; Handwriting recognition; Historical manuscript analysis BibRef

Alabau, V.[Vicent], Sanchis, A.[Alberto], Casacuberta, F.[Francisco],
On the optimal decision rule for sequential interactive structured prediction,
PRL(33), No. 16, 1 December 2012, pp. 2226-2231.
Elsevier DOI 1210
Interactive pattern recognition; Minimum Bayes risk; Human interaction; Machine translation; Handwritten text recognition; Automatic speech recognition BibRef

Alabau, V.[Vicent], Sanchis, A.[Alberto], Casacuberta, F.[Francisco],
Improving on-line handwritten recognition in interactive machine translation,
PR(47), No. 3, 2014, pp. 1217-1228.
Elsevier DOI 1312
Interactive pattern recognition BibRef

Tarazón, L.[Lionel], Pérez, D.[Daniel], Serrano, N.[Nicolás], Alabau, V.[Vicent], Terrades, O.R.[Oriol Ramos], Sanchis, A.[Alberto], Juan, A.[Alfons],
Confidence Measures for Error Correction in Interactive Transcription Handwritten Text,
CIAP09(567-574).
Springer DOI 0909
BibRef

Wshah, S.[Safwan], Kumar, G.[Gaurav], Govindaraju, V.[Venu],
Statistical script independent word spotting in offline handwritten documents,
PR(47), No. 3, 2014, pp. 1039-1050.
Elsevier DOI 1312
BibRef
Earlier:
Multilingual word spotting in offline handwritten documents,
ICPR12(310-313).
WWW Link. 1302
Script independent BibRef

van Oosten, J.P.[Jean-Paul], Schomaker, L.[Lambert],
Separability versus prototypicality in handwritten word-image retrieval,
PR(47), No. 3, 2014, pp. 1031-1038.
Elsevier DOI 1312
BibRef
Earlier:
Separability versus Prototypicality in Handwritten Word Retrieval,
FHR12(8-13).
IEEE DOI 1302
Image retrieval BibRef

Rusiñol, M.[Marçal], Lladós, J.[Josep],
Boosting the handwritten word spotting experience by including the user in the loop,
PR(47), No. 3, 2014, pp. 1063-1072.
Elsevier DOI 1312
Handwritten word spotting BibRef

Kessentini, Y.[Yousri], Burger, T.[Thomas], Paquet, T.[Thierry],
A Dempster-Shafer Theory based combination of handwriting recognition systems with multiple rejection strategies,
PR(48), No. 2, 2015, pp. 534-544.
Elsevier DOI 1411
BibRef
Earlier: A2, A1, A3:
Dempster-Shafer Based Rejection Strategy for Handwritten Word Recognition,
ICDAR11(528-532).
IEEE DOI 1111
BibRef
Earlier: A2, A1, A3:
Dealing with Precise and Imprecise Decisions with a Dempster-Shafer Theory Based Algorithm in the Context of Handwritten Word Recognition,
FHR10(369-374).
IEEE DOI 1011
Handwriting recognition BibRef

Kessentini, Y.[Yousri], Paquet, T.[Thierry], Ben-Hamadou, A.M.[Abdel Majid],
A Multi-Lingual Recognition System for Arabic and Latin Handwriting,
ICDAR09(1196-1200).
IEEE DOI 0907
BibRef
Earlier:
Multi-script handwriting recognition with N-streams low level features,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Zhang, X.[Xi], Tan, C.L.[Chew Lim],
Handwritten word image matching based on Heat Kernel Signature,
PR(48), No. 11, 2015, pp. 3346-3356.
Elsevier DOI 1506
BibRef
Earlier: CAIP13(II:42-49).
Springer DOI 1311
Handwritten documents BibRef

Chen, J.Y.[Jin-Ying], Cao, H.G.[Huai-Gu], Natarajan, P.[Premkumar],
Integrating natural language processing with image document analysis: what we learned from two real-world applications,
IJDAR(18), No. 3, September 2015, pp. 235-247.
Springer DOI 1509
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Roy, P.P.[Partha Pratim], Rayar, F.[Frédéric], Ramel, J.Y.[Jean-Yves],
Word spotting in historical documents using primitive codebook and dynamic programming,
IVC(44), No. 1, 2015, pp. 15-28.
Elsevier DOI 1601
Word spotting BibRef

Kumar, G.[Gaurav], Govindaraju, V.[Venu],
Bayesian background models for keyword spotting in handwritten documents,
PR(64), No. 1, 2017, pp. 84-91.
Elsevier DOI 1701
BibRef
Earlier:
Bayesian Active Learning for Keyword Spotting in Handwritten Documents,
ICPR14(2041-2046)
IEEE DOI 1412
Handwriting recognition. Bayes methods BibRef

Wshah, S.[Safwan], Kumar, G.[Gaurav], Govindaraju, V.[Venu],
Script Independent Word Spotting in Offline Handwritten Documents Based on Hidden Markov Models,
FHR12(14-19).
IEEE DOI 1302
BibRef

Giotis, A.P.[Angelos P.], Sfikas, G.[Giorgos], Gatos, B.[Basilis], Nikou, C.[Christophoros],
A survey of document image word spotting techniques,
PR(68), No. 1, 2017, pp. 310-332.
Elsevier DOI 1704
Survey, Word Spotting. BibRef
Earlier: A1, A2, A4, A3:
Shape-based word spotting in handwritten document images,
ICDAR15(561-565)
IEEE DOI 1511
Word spotting handwritten text BibRef

Mhiri, M.[Mohamed], Abuelwafa, S.[Sherif], Desrosiers, C.[Christian], Cheriet, M.[Mohamed],
Hierarchical representation learning using spherical k-means for segmentation-free word spotting,
PRL(101), No. 1, 2018, pp. 52-59.
Elsevier DOI 1801
Segmentation-free and training-free word spotting BibRef

Mhiri, M.[Mohamed], Cheriet, M.[Mohamed], Desrosiers, C.[Christian],
Query-by-example word spotting using multiscale features and classification in the space of representation differences,
ICIP17(1112-1116)
IEEE DOI 1803
Encoding, Euclidean distance, Feature extraction, Robustness, Support vector machines, Task analysis, Training, word spotting BibRef

Moghaddam, R.F.[Reza Farrahi], Cheriet, M.[Mohamed],
Application of Multi-Level Classifiers and Clustering for Automatic Word Spotting in Historical Document Images,
ICDAR09(511-515).
IEEE DOI 0907

See also multi-scale framework for adaptive binarization of degraded document images, A. BibRef

Dong, X., Dong, J.,
The Visual Word Booster: A Spatial Layout of Words Descriptor Exploiting Contour Cues,
IP(27), No. 8, August 2018, pp. 3904-3917.
IEEE DOI 1806
feature extraction, image classification, image matching, image representation, image texture, SLoW descriptor, spatial layout BibRef

Mhiri, M.[Mohamed], Desrosiers, C.[Christian], Cheriet, M.[Mohamed],
Convolutional pyramid of bidirectional character sequences for the recognition of handwritten words,
PRL(111), 2018, pp. 87-93.
Elsevier DOI 1808
Word recognition, Offline handwriting, Word representation, Deep convolutional neural networks BibRef

Gupta, J.D.[Jija Das], Samanta, S.[Soumitra], Chanda, B.[Bhabatosh],
Ensemble classifier-based off-line handwritten word recognition system in holistic approach,
IET-IPR(12), No. 8, August 2018, pp. 1467-1474.
DOI Link 1808
BibRef

Sudholt, S.[Sebastian], Fink, G.A.[Gernot A.],
Attribute CNNs for word spotting in handwritten documents,
IJDAR(21), No. 3, September 2018, pp. 199-218.
Springer DOI 1810
BibRef

Wu, X., Chen, Q., You, J., Xiao, Y.,
Unconstrained Offline Handwritten Word Recognition by Position Embedding Integrated ResNets Model,
SPLetters(26), No. 4, April 2019, pp. 597-601.
IEEE DOI 1903
Hidden Markov models, Handwriting recognition, Feature extraction, Text recognition, Training, Linguistics, off-line handwritten word recognition BibRef

Retsinas, G.[George], Louloudis, G.[Georgios], Stamatopoulos, N.[Nikolaos], Gatos, B.[Basilis],
Efficient Learning-Free Keyword Spotting,
PAMI(41), No. 7, July 2019, pp. 1587-1600.
IEEE DOI 1906
BibRef
Earlier: A4, A2, A3, Only:
Greek Polytonic OCR Based on Efficient Character Class Number Reduction,
ICDAR11(1155-1159).
IEEE DOI 1111
Image segmentation, Feature extraction, Training, Hidden Markov models, Writing, Euclidean distance, Task analysis, sequence matching BibRef

Stamatopoulos, N.[Nikolaos], Louloudis, G.[Georgios], Gatos, B.[Basilis],
Efficient Transcript Mapping to Ease the Creation of Document Image Segmentation Ground Truth with Text-Image Alignment,
FHR10(226-231).
IEEE DOI 1011
Ground truth creation. BibRef

Hou, J., Shi, Y., Ostendorf, M., Hwang, M., Xie, L.,
Region Proposal Network Based Small-Footprint Keyword Spotting,
SPLetters(26), No. 10, October 2019, pp. 1471-1475.
IEEE DOI 1909
Feature extraction, Hidden Markov models, Training, Proposals, Object detection, Backpropagation, Vocabulary, Keyword spotting, false alarm BibRef

Barrere, K.[Killian], Toselli, A.H.[Alejandro H.], Vidal, E.[Enrique],
Line Segmentation Free Probabilistic Keyword Spotting and Indexing,
IbPRIA19(II:201-213).
Springer DOI 1910
BibRef

Krishnan, P.[Praveen], Jawahar, C.V.,
HWNet v2: an efficient word image representation for handwritten documents,
IJDAR(22), No. 4, December 2019, pp. 387-405.
Springer DOI 1911
BibRef

Dey, S.[Sounak], Nicolaou, A.[Anguelos], Lladós, J.[Josep], Pal, U.[Umapada],
Evaluation of word spotting under improper segmentation scenario,
IJDAR(22), No. 4, December 2019, pp. 361-374.
Springer DOI 1911
BibRef

Bera, S.K.[Suman Kumar], Chakrabarti, A.[Akash], Lahiri, S.[Sagnik], Smith, E.H.B.[Elisa H. Barney], Sarkar, R.[Ram],
Normalization of unconstrained handwritten words in terms of Slope and Slant Correction,
PRL(128), 2019, pp. 488-495.
Elsevier DOI 1912
BibRef

Das, D.[Dibyasundar], Nayak, D.R.[Deepak Ranjan], Dash, R.[Ratnakar], Majhi, B.[Banshidhar], Zhang, Y.D.[Yu-Dong],
H-WordNet: a holistic convolutional neural network approach for handwritten word recognition,
IET-IPR(14), No. 9, 20 July 2020, pp. 1794-1805.
DOI Link 2007
BibRef

Mohammed, H.H.[Hanadi Hassen], Subramanian, N.[Nandhini], Al-Madeed, S.[Somaya],
Learning-free handwritten word spotting method for historical handwritten documents,
IET-IPR(15), No. 10, 2021, pp. 2332-2341.
DOI Link 2108
BibRef

Boudraa, O.[Omar], Michelucci, D.[Dominique], Hidouci, W.K.[Walid Khaled],
PUNet: Novel and efficient deep neural network architecture for handwritten documents word spotting,
PRL(155), 2022, pp. 19-26.
Elsevier DOI 2203
Deep learning, Word spotting, Information retrieval, Transfer learning, Convolutional neural network, Pyramidal histogram of characters BibRef

Huang, X.H.[Xiang-Hong], Yang, Q.[Qun], Liu, S.H.[Shao-Han],
Depthwise-Separable Residual Capsule for Robust Keyword Spotting,
MMMod22(II:194-204).
Springer DOI 2203
BibRef

Kim, D.[Donghyeon], Kim, G.[Gwantae], Lee, B.[Bokyeung], Ko, H.S.[Han-Seok],
Prototypical Knowledge Distillation for Noise Robust Keyword Spotting,
SPLetters(29), 2022, pp. 2298-2302.
IEEE DOI 2212
Computational modeling, Feature extraction, Training, Mathematical models, Convolution, Convolutional neural networks, prototypical learning BibRef

Mhiri, M.[Mohamed], Hamdan, M.[Mohammed], Cheriet, M.[Mohamed],
Handwriting word spotting in the space of difference between representations using vision transformers,
PRL(174), 2023, pp. 39-45.
Elsevier DOI 2310
Word spotting, Query-by-example, Query-by-string, PBSC representation, CNN, Vision transformer BibRef

Kim, Y.[Youkyum], Jung, J.[Jaemin], Park, J.[Jihwan], Kim, B.Y.[Byeong-Yeol], Chung, J.S.[Joon Son],
Bridging the Gap Between Audio and Text Using Parallel-Attention for User-Defined Keyword Spotting,
SPLetters(31), 2024, pp. 2100-2104.
IEEE DOI 2408
Training, Feature extraction, Indexes, Convolution, Kernel, Vectors, Benchmark testing, Attention mechanism, multi-modal fusion, user-defined keyword spotting BibRef

Wolf, F.[Fabian], Fink, G.A.[Gernot A.],
Self-Training of Handwritten Word Recognition for Synthetic-to-Real Adaptation,
ICPR22(3885-3892)
IEEE DOI 2212
Training, Deep learning, Adaptation models, Handwriting recognition, Text analysis, Text recognition, Estimation BibRef

Prieto, J.R.[Jose Ramón], Vidal, E.[Enrique], Sánchez, J.A.[Joan Andreu], Alonso, C.[Carlos], Garrido, D.[David],
Extracting Descriptive Words from Untranscribed Handwritten Images,
IbPRIA22(540-551).
Springer DOI 2205
BibRef

Rosello, A.[Adrian], Ayllon, E.[Eric], Valero-Mas, J.J.[Jose J.], Calvo-Zaragoza, J.[Jorge],
Test Sample Selection for Handwriting Recognition Through Language Modeling,
IbPRIA22(3-13).
Springer DOI 2205
BibRef

Guo, J.J.[Jing-Jie], Tian, K.[Kelang], Ye, K.J.[Ke-Jiang], Xu, C.Z.[Cheng-Zhong],
MA-LSTM: A Multi-Attention Based LSTM for Complex Pattern Extraction,
ICPR21(3605-3611)
IEEE DOI 2105
Deep learning, Handwriting recognition, Recurrent neural networks, Computer architecture, language model BibRef

Chakraborty, A.[Anuran], De, R.[Rajonya], Malakar, S.[Samir], Schwenker, F.[Friedhelm], Sarkar, R.[Ram],
Handwritten Digit String Recognition using Deep Autoencoder based Segmentation and ResNet based Recognition Approach,
ICPR21(7737-7742)
IEEE DOI 2105
Deep learning, Handwriting recognition, Image recognition, Databases, Computational modeling, U-Net BibRef

Bhattacharya, R.[Rajdeep], Malakar, S.[Samir], Schwenker, F.[Friedhelm], Sarkar, R.[Ram],
Fuzzy-based Pseudo Segmentation Approach for Handwritten Word Recognition Using a Sequence to Sequence Model with Attention,
DLPR20(582-596).
Springer DOI 2103
BibRef

Kang, L., Rusiñol, M., Fornés, A., Riba, P., Villegas, M.,
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition,
WACV20(3491-3500)
IEEE DOI 2006
Handwriting recognition, Feature extraction, Adaptation models, Hidden Markov models, Target recognition, Training, Training data BibRef

Kang, L.[Lei], Toledo, J.I.[J. Ignacio], Riba, P.[Pau], Villegas, M.[Mauricio], Fornés, A.[Alicia], Rusiñol, M.[Marçal],
Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition,
GCPR18(459-472).
Springer DOI 1905
BibRef

Axler, G., Wolf, L.,
Toward a Dataset-Agnostic Word Segmentation Method,
ICIP18(2635-2639)
IEEE DOI 1809
Proposals, Heating systems, Task analysis, Image segmentation, Training, Object detection, Integrated circuits, Object Detection, Transfer Learning BibRef

Wilkinson, T., Lindström, J., Brun, A.,
Neural Ctrl-F: Segmentation-Free Query-by-String Word Spotting in Handwritten Manuscript Collections,
ICCV17(4443-4452)
IEEE DOI 1802
document image processing, handwritten character recognition, image segmentation, Text recognition BibRef