Skip to main content
SpringerLink
Log in

Importance and challenges of handwriting recognition with the implementation of machine learning techniques: a survey

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Ancient manuscripts store historical, literary, cultural, and geographical information. Therefore, the automatic analysis of manuscripts is of great interest in heritage culture and history preservation. Different approaches to handwriting recognition using images have been applied to analyze manuscripts. However, reliable handwriting recognition is a considerable challenge due to different factors related to the writer, the design, the script, the manuscript, and the economy. This paper presents the most relevant works in handwriting recognition using machine learning techniques. The contributions are: i) provide a review of previous research addressing handwriting recognition, ii) depict the general methodology using machine learning in handwriting recognition, iii) highlight relevant works at different levels of analysis (character, word, text line, and text block), iv) present handwriting datasets including the type of content they have, script and language, and v) present the importance and challenges in handwriting recognition. We are confident that the insights and reflections from this review will have a positive impact on the gaps for future research in handwriting recognition.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Algorithm 1
Algorithm 2
Algorithm 3
Algorithm 4
Algorithm 5
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. https://www.sciencedirect.com

  2. https://ieeexplore.ieee.org

  3. https://link.springer.com

  4. https://www.tandfonline.com

  5. https://scholar.google.com

  6. There are more libraries, but those with the largest number of manuscripts and even multimedia elements are mentioned in this review.

References

  1. Van Galen GP (1991) Handwriting: issues for a psychomotor theory. Hum Mov Sci 10(2–3):165–191

  2. Muñoz y Rivero, J.: Manual de paleografía diplomática española de los siglos XII al XVII: método teórico-práctico. Imprenta de Moreno y Rojas (1889)

  3. Srivastava S, Verma A, Sharma S (2022) Optical character recognition techniques: a review. In: 2022 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), IEEE, pp 1–6

  4. Ali AAA, Suresha M (2019) A novel features and classifiers fusion technique for recognition of arabic handwritten character script. SN Appl Sci 1(10):1286

    Article  Google Scholar 

  5. Ahmed RM, Rashid TA, Fattah P, Alsadoon A, Bacanin N, Mirjalili S, Vimal S, Chhabra A (2022) Kurdish handwritten character recognition using deep learning techniques. Gene Expr Patterns 46:119278

    Article  Google Scholar 

  6. Alaei F, Alaei A (2023) Review of age and gender detection methods based on handwriting analysis. Neural Comput Appl 35(33):23909–23925

    Article  Google Scholar 

  7. Rabaev I, Litvak M (2023) Automated gender classification from handwriting: a systematic survey. Appl Intell 53(13):17154–17177

    Article  Google Scholar 

  8. De Stefano C, Fontanella F, Impedovo D, Pirlo G, di Freca AS (2019) Handwriting analysis to support neurodegenerative diseases diagnosis: a review. Pattern Recogn Lett 121:37–45

  9. Mehta N, Doshi J (2020) Text line segmentation for medieval devnagari manuscript. In: Proceedings of international conference on communication and computational technologies. ICCCT ’19, Springer, Singapore, SG, pp 405–412

  10. Shapira DV, Rabaev I, Barakat BK, Droby A, El-Sana J (2020) Deep learning for paleographic analysis of medieval hebrew manuscripts: a dh team collaboration experience. In: Twin Talks: understanding and facilitating collaboration in digital humanities 2020. DHN ’20

  11. of Washington Libraries U (2021) History: Manuscript Collections. https://guides.lib.uw.edu/c.php?g=341342 &p=2298253

  12. Abdullah AA, Delfina AD (2020) The text analysis of poesponegoro manuscript. In: International conference on english language teaching (ICONELT 2019). Advances in social science, education and humanities research, Atlantis Press, pp 9–11

  13. Martínez LP (1985) Metodología de la historia: La paleografía y la diplomática. In: Anales de la Universidad de Murcia. Letras

  14. Bennour A, Djeddi C, Gattal A, Siddiqi I, Mekhaznia T (2019) Handwriting based writer recognition using implicit shape codebook. Forensic Sci Int 301(17):91–100. https://doi.org/10.17485/IJST/v13i17.113

  15. Bluche T, Stutzmann D, Kermorvant C (2016) Automatic handwritten character segmentation for paleographical character shape analysis. 2016 12th IAPR Workshop on document analysis systems (DAS). IAPR ’16. IEEE, Santorini, GR, pp 42–47

  16. Kavitha AS, Shivakumara P, Kumar GH, Lu T (2016) Text segmentation in degraded historical document images. Egypt Inform J 17(2):189–197. https://doi.org/10.1016/j.eij.2015.11.003

  17. Aqab S, Tariq MU (2020) Handwriting recognition using artificial intelligence neural network and image processing. Int J Adv Comput Sci Appl 11(7):137–146. https://doi.org/10.14569/ijacsa.2020.0110719

  18. Burrows S, Falk M (2021) Digital humanities. In: Oxford Research Encyclopedia of Literature, Oxford University Press., pp 1–24. https://oxfordre.com/literature/view/10.1093/acrefore/9780190201098.001.0001/acrefore-9780190201098-e-971

  19. Augustin E, Carré M, Grosicki E, Brodin J-M, Geoffrois E, Prêteux F (2006) Rimes evaluation campaign for handwritten mail processing. In: International workshop on frontiers in handwriting recognition (IWFHR’06), pp 231–235

  20. Ghosh M, Mukherjee H, Obaidullah SM, Santosh KC, Das N, Roy K (2020) Artistic multi-script identification at character level with extreme learning machine. Procedia Comput Sci 167:496–505. https://doi.org/10.1016/j.procs.2020.03.2683

  21. Toledo JI (2019) Information extraction from heterogeneous handwritten documents. PhD thesis, Universitat Autónoma de Barcelona, Barcelona, España

  22. Wejéus S (2014) A neural network approach to arbitrary symbolrecognition on modern smartphones. Master’s thesis, Royal Institute of Technology (KTH), Stockholm, Sweden

  23. Basu S, Chaudhuri C, Kundu M, Nasipuri M, Basu DK (2007) Text line extraction from multi-skewed handwritten documents. Pattern Recogn 40(6):1825–1839

    Article  Google Scholar 

  24. Ghosh T, Sen S, Obaidullah SM, Santosh K, Roy K, Pal U (2022) Advances in online handwritten recognition in the last decades. Computer Science Review 46:100515

    Article  MathSciNet  Google Scholar 

  25. Zhang H, Liang L, Jin L (2020) Scut-hccdoc: a new benchmark dataset of handwritten chinese text in unconstrained camera-captured documents. Pattern Recogn 108:107559

  26. Inunganbi S (2024) A systematic review on handwritten document analysis and recognition. Multimedia Tools and Applications 83(2):5387–5413

    Article  Google Scholar 

  27. Chhajro MA, Khan H, Khan F, Kumar K, Wagan AA, Solangi S (2020) Handwritten urdu character recognition via images using different machine learning and deep learning techniques. Indian J Sci Technol 13(17):1746–1754. https://doi.org/10.17485/IJST/v13i17.113

  28. Ahlawat S, Choudhary A (2020) Hybrid cnn-svm classifier for handwritten digit recognition. Procedia Computer Science 167:2554–2560. https://doi.org/10.1016/j.procs.2020.03.309

    Article  Google Scholar 

  29. Fernandes R, Rodrigues AP (2019) Kannada handwritten script recognition using machine learning techniques. 2019 IEEE International conference on distributed computing, VLSI, Electrical Circuits and Robotics (DISCOVER). DISCOVER ’19. IEEE, Manipal, IN, pp 1–6

  30. Ali AAA, Mallaiah S (2022) Intelligent handwritten recognition using hybrid cnn architectures based-svm classifier with dropout. Journal of King Saud University-Computer and Information Sciences 34(6):3294–3300

    Article  Google Scholar 

  31. Mondal R, Malakar S, Barney Smith EH, Sarkar R (2022) Handwritten english word recognition using a deep learning based object detection architecture. Multimed Tool Appl pp 1–26

  32. Kaur M, Saini K (2022) Forensic examination of effects of parkinsonism on various handwriting characteristics. Science & Justice 62(1):10–20

    Article  Google Scholar 

  33. Castro D, Zanchettin C, Amaral LAN (2024) On the improvement of handwritten text line recognition with octave convolutional recurrent neural networks. Int J Doc Anal Recog (IJDAR), pp 1–15

  34. Lehenmeier C, Burghardt M, Mischka B (2020) Layout detection and table recognition–recent challenges in digitizing historical documents and handwritten tabular data. In: International conference on theory and practice of digital libraries. LNCS 12246. Springer, Switzerland, AG

  35. Dolfing HJGA, Bellegarda J, Chorowski J, Marxer R, Laurent A (2020) The “scribblelens” dutch historical handwriting corpus. 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR). ICFHR ’20. IEEE, Dortmund, DE, pp 67–72

  36. Chychkarov Y, Serhiienko A, Syrmamiikh I, Kargin A (2021) Handwritten digits recognition using svm, knn, rf and deep learning neural networks. CMIS 2864:496–509

    Google Scholar 

  37. Yogesh Y, Ghantasala GP, Priya A (2023) Artificial intelligence based handwriting digit recognition (hdr)-a technical review. In: 2023 International conference on device intelligence, computing and communication technologies,(DICCT), IEEE, pp 275–278

  38. Sharma R, Kaushik B (2020) Offline recognition of handwritten indic scripts: a state-of-the-art survey and future perspectives. Comput Sci Rev 38:100302. https://doi.org/10.1016/j.cosrev.2020.100302

  39. Ali AAA, Suresha M, Ahmed HAM (2020) A survey on arabic handwritten character recognition. SN Comput Sci 1(3):152

    Article  Google Scholar 

  40. Ali AAA, Suresha M (2020) Survey on segmentation and recognition of handwritten arabic script. SN Comput Sci 1(4):192

    Article  Google Scholar 

  41. Memon J, Sami M, Khan RA, Uddin M (2020) Handwritten optical character recognition (ocr): a comprehensive systematic literature review (slr). IEEE Access 8:142642–142668. https://doi.org/10.1109/ACCESS.2020.3012542

  42. Singh H, Sharma RK, Singh V (2021) Online handwriting recognition systems for indic and non-indic scripts: a review. Artif Intell Rev 54:1525–1579

    Article  Google Scholar 

  43. Balaha HM, Ali HA, Badawy M (2021) Automatic recognition of handwritten arabic characters: a comprehensive review. Neural Comput Appl 33:3011–3034

    Article  Google Scholar 

  44. Singh S, Sharma A, Chauhan VK (2023) Indic script family and its offline handwriting recognition for characters/digits and words: a comprehensive survey. Artif Intell Rev 56(Suppl 3):3003–3055

    Article  Google Scholar 

  45. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37–54. https://doi.org/10.1609/aimag.v17i3.1230

    Article  Google Scholar 

  46. Leung CK (2021) Big data analytics and mining for knowledge discovery. In: Encyclopedia of Organizational Knowledge, Administration, and Technology, IGI Global, pp 1817–1830. https://www.igi-global.com/chapter/big-data-analytics-and-mining-for-knowledge-discovery/263656

  47. Drucker J (2021) The Digital Humanities Coursebook: An Introduction to Digital Methods for Research and Scholarship. Routledge

  48. Library B (2021) Catalogues and Collections. https://www.bl.uk/catalogues-and-collections

  49. Library B, Digitised Manuscripts Home. https://www.bl.uk/manuscripts/

  50. Collections E, About Europeana. https://www.europeana.eu/es/about-us

  51. Collections E, Manuscripts. https://www.europeana.eu/es/collections/topic/17-manuscripts

  52. Archive I, About the Internet Archive. https://archive.org/about/

  53. Archive TI, Hebrew Manuscripts. https://archive.org/details/culhebrewmss

  54. Archive TI, Muslim World Manuscripts Collection. https://archive.org/details/muslim-world-manuscripts

  55. Hayden C, About the Library. https://archive.org/about/

  56. of Congress TL (2020) About the Manuscript Division. https://www.loc.gov/rr/mss/mss_abt.html

  57. of Israel TNL (2021) The National Library of Israel. https://www.nli.org.il/en

  58. de España BN (2021) Colecciones especiales. http://catalogo.bne.es/uhtbin/cgisirsi/?ps=Ju7deVSadO/BNMADRID/114030428/1/106/X/BLASTOFF

  59. de España BN (2021) Manuscritos. http://www.bne.es/es/Colecciones/Manuscritos/

  60. of Chicago TU (2015) Library Catalog. https://catalog.lib.uchicago.edu/vufind/Search/Advanced

  61. of Chicago TU, About Manuscript Collections. https://www.lib.uchicago.edu/scrc/manuscript/about-manuscript-collections/

  62. of Washington Libraries U (2021) History: Manuscript Collections. https://guides.lib.uw.edu/c.php?g=341342 &p=2298253

  63. Library V, Vatican Library. https://digi.vatlib.it/

  64. Library V, Manuscripts List. https://digi.vatlib.it/mss/

  65. Library WD, About the World Digital Library. https://www.wdl.org/en/about/

  66. Library WD, Manuscripts. https://www.wdl.org/en/search/?item_type=manuscript

  67. Saady YE, Rachidi A, Yassa M, Mammass D (2011) Tamhcd: a database for amazigh handwritten character recognition research. Int J Comput Appl 27(4):44–48. https://doi.org/10.5120/3286-4475

  68. Sadouk L, Gadi T, Essoufi EH (2017) Handwritten tifinagh character recognition using deep learning architectures. In: Proceedings of the 1st international conference on internet of things and machine learning, pp 1–11

  69. Benaddy M, Meslouhi OE, Es-saady Y, Kardouchi M (2019) Handwritten tifinagh characters recognition using deep convolutional neural networks. Sensing and Imaging 20(1):1–17. https://doi.org/10.1109/ICAICT51780.2020.9333472

    Article  Google Scholar 

  70. Biswas M, Islam R, Shom GK, Shopon M, Mohammed N, Momen S, Abedin A (2017) Banglalekha-isolated: a multi-purpose comprehensive dataset of handwritten bangla isolated characters. Data Brief 12:103–107. https://doi.org/10.1016/j.dib.2017.03.035

  71. Azad MA, Singha HS, Nahid MMH (2020) Bangla handwritten character recognition using deep convolutional autoencoder neural network. In: 2020 2nd International conference on advanced information and communication technology (ICAICT), IEEE, pp 295–300

  72. LeCun Y (1998) The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist/

  73. Cohen G, Afshar S, Tapson J, van Schaik A (2017) Emnist: extending mnist to handwritten letters. Int Joint Conf Neural Netw (IJCNN). IJCNN ’17. IEEE, Anchorage, AK, USA, pp 2921–2926

  74. Kim C-M, Hong EJ, Chung K, Park RC (2020) Line-segment feature analysis algorithm using input dimensionality reduction for handwritten text recognition. Appl Sci 10(19):6904. https://doi.org/10.3390/app10196904

    Article  Google Scholar 

  75. G R, Sharma GN, Balaji JM, HN C (2019) Offline kannada handwritten character recognition using convolutional neural networks. In: 2019 IEEE International WIE conference on electrical and computer engineering (WIECON-ECE). WIECON-ECE ’19, IEEE, Bangalore, IN, pp 1–5

  76. Romero V, Fornés A, Serrano N, Sánchez JA, H Toselli A, Frinken V, Vidal E, Lladós J (2013) The esposalles database: an ancient marriage license corpus for off-line handwriting recognition. Pattern Recog 46(6):1658–1669. https://doi.org/10.1016/j.patcog.2012.11.024

  77. Wu X, Chen Q, You J, Xiao Y (2019) Unconstrained offline handwritten word recognition by position embedding integrated resnets model. IEEE Signal Process Lett 26(4):597–601. https://doi.org/10.1109/LSP.2019.2895967

    Article  Google Scholar 

  78. Wshah S, Kumar G, Govindaraju V (2012) Script independent word spotting in offline handwritten documents based on hidden markov models. 2012 International conference on frontiers in handwriting recognition. ICFHR ’12. IEEE, Bari, IT, pp 14–19

  79. Marti U-V, Bunke H (2002) The iam-database: an english sentence database for offline handwriting recognition. Int J Doc Anal Recogn 5(1):39–46. https://doi.org/10.1007/s100320200071

    Article  Google Scholar 

  80. Voigtlaender P, Doetsch P, Ney H (2016) Handwriting recognition with large multidimensional long short-term memory recurrent neural networks. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE, pp 228–233

  81. Diem M, Kleber F, Fiel S, Grüning T, Gatos B (2017) ScriptNet: ICDAR 2017 Competition on Baseline Detection in Archival Documents (cBAD). https://zenodo.org/record/835441

  82. Regensburg U (2010) Observationes meteorologicae. https://rzbvm050.uni-regensburg.de/meteorologie/

  83. Kleber F, Fiel S, Diem M, Sablatnig R (2013) Cvl-database: An off-line database for writer retrieval, writer identification and word spotting. In: 2013 12th International conference on document analysis and recognition, IEEE, pp 560–564

  84. Freitas COA, Oliveira LS, Sabourin R, Bortolozzi F (2008) Brazilian forensic letter database. In: In 11th international workshop on frontiers on handwriting recognition

  85. Casey RG, Lecolinet E (1996) A survey of methods and strategies in character segmentation. IEEE Trans Pattern Anal Mach Intell 18(7):690–706. https://doi.org/10.1109/34.506792

    Article  Google Scholar 

  86. Koloda J, Wang J (2023) Context aware document binarization and its application to information extraction from structured documents. In: International conference on document analysis and recognition, Springer, pp 63–78

  87. Akhand M, Ahmed M, Rahman MH, Islam MM (2018) Convolutional neural network training incorporating rotation-based generated patterns and handwritten numeral recognition of major indian scripts. IETE J Res 64(2):176–194. https://doi.org/10.1080/03772063.2017.1351322

    Article  Google Scholar 

  88. Khayyat MM, Elrefaei LA (2020) Towards author recognition of ancient arabic manuscripts using deep learning: a transfer learning approach. International Journal of Computing and Digital Systems 9(5):1–18

  89. Husnain M, Missen MMS, Mumtaz S, Jhanidr MZ, Coustaty M, Luqman MM, Ogier J-M, Choi GS (2019) Recognition of urdu handwritten characters using convolutional neural network. Appl Sci 9(13):2758. https://doi.org/10.3390/app9132758

    Article  Google Scholar 

  90. Maliki I, Prayoga A (2023) Implementation of convolutional neural network for sundanese script handwriting recognition with data augmentation. J Eng Sci Technol 18(2):1113–1123

    Google Scholar 

  91. Tran VN, Huang C-E, Liu S-H, Aslam MS, Yang K-L, Li Y-H, Wang J-C (2024) Multi-view and multi-augmentation for self-supervised visual representation learning. Appl Intell 54(1):629–656

    Article  Google Scholar 

  92. de Sousa Neto AF, Bezerra BLD, de Moura GCD, Toselli AH (2024) Data augmentation for offline handwritten text recognition: a systematic literature review. SN Comput Sci 5(2):258

  93. Hinds SC, Fisher JL, D’Amato DP (1990) A document skew detection method using run-length encoding and the hough transform. [1990] Proceedings. 10th International conference on pattern recognition. IEEE, USA, US, pp 464–468

  94. Rizvi MAI, Deb K, Khan MI, Kowsar MMS, Khanam T (2019) A comparative study on handwritten bangla character recognition. Turk J Electr Eng Comput Sci 27(4):3195–3207. https://doi.org/10.3906/elk-1901-48

  95. Mhiri M, Desrosiers C, Cheriet M (2019) Word spotting and recognition via a joint deep embedding of image and text. Pattern Recogn 88:312–320. https://doi.org/10.1016/j.patcog.2018.11.017

    Article  Google Scholar 

  96. Serra J (1986) Introduction to mathematical morphology. Computer vision, graphics, and image processing 35(3):283–305. https://doi.org/10.1016/0734-189X(86)90002-2

    Article  Google Scholar 

  97. Shivakumara P, Sreedhar RP, Phan TQ, Lu S, Tan CL (2012) Multioriented video scene text detection through bayesian classification and boundary growing. IEEE Trans Circuits Syst Video Technol 22(8):1227–1235. https://doi.org/10.1109/TCSVT.2012.2198129

    Article  Google Scholar 

  98. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. https://doi.org/10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  99. Haliassos A, Barmpoutis P, Stathaki T, Quirke S, Constantinides A (2020) Classification and detection of symbols in ancient papyri. In: Visual computing for cultural heritage, Springer, Switzerland, AG, pp 121–140. https://doi.org/10.1007/978-3-030-37191-3_7

  100. Firdaus SA, Vaidehi K (2020) Handwritten mathematical symbol recognition using machine learning techniques: review. In: Advances in decision sciences, image processing, security and computer vision, Springer, Switzerland, AG, pp 658–671. https://doi.org/10.1007/978-3-030-24318-0_75

  101. Ali I, Ali I, Subhash AK, Raza SA, Hassan B, Bhatti P (2019) Sindhi handwritten-digits recognition using machine learning techniques. Int J Comput Sci Netw Secur 19(5):195–202

  102. Keshta IM (2017) Handwritten digit recognition based on output-independent multi-layer perceptrons. HAND 8(6):26–31. https://doi.org/10.14569/IJACSA.2017.080604

  103. Tavoli R, Keyvanpour M (2017) A method for handwritten word spotting based on particle swarm optimisation and multi-layer perceptron. IET Software 12(2):152–159. https://doi.org/10.1049/iet-sen.2017.0071

    Article  Google Scholar 

  104. Cilia ND, Stefano CD, Fontanella F, Marrocco C, Molinara M, Freca ASD (2019) A two-step system based on deep transfer learning for writer identification in medieval books. International conference on computer analysis of images and patterns. LNCS 11679. Springer, Switzerland, AG, pp 305–316

  105. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1007/BF00994018

    Article  Google Scholar 

  106. Sadri J, Suen CY, Bui TD (2003) Application of support vector machines for recognition of handwritten arabic/persian digits. In: Proceedings of second iranian conference on machine vision and image processing

  107. Charalambous E, Dikomitou-Eliadou M, Milis GM, Mitsis G, Eliades DG (2015) An experimental design for the classification of archaeological ceramic data from cyprus, and the tracing of inter-class relationships. J Archaeol Sci Rep 7:465–471. https://doi.org/10.1016/j.jasrep.2015.08.010

    Article  Google Scholar 

  108. Nam Nguyen QD, Liu A-B, Lin C-W (2020) Development of a neurodegenerative disease gait classification algorithm using multiscale sample entropy and machine learning classifiers. Entropy 22(12):1340

    Article  Google Scholar 

  109. Fix E, Hodges Jr JL (1952) Discriminatory analysis-nonparametric discrimination: small sample performance. Technical report, University of California, Berkeley, Berkeley, CA, USA

  110. Liu T, Wang J, Yang B, Wang X (2021) Ngdnet: nonuniform gaussian-label distribution learning for infrared head pose estimation and on-task behavior understanding in the classroom. Neurocomputing 436:210–220

  111. Frias-Martinez E, Sanchez A, Velez J (2006) Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition. Eng Appl Artif Intell 19(6):693–704. https://doi.org/10.1016/j.engappai.2005.12.006

    Article  Google Scholar 

  112. Ortigosa EM, Cañas A, Ros E, Ortigosa PM, Mota S, Díaz J (2006) Hardware description of multi-layer perceptrons with different abstraction levels. Microprocess Microsyst 30(7):435–444. https://doi.org/10.1016/j.micpro.2006.03.004

    Article  Google Scholar 

  113. Murtagh F (1991) Multilayer perceptrons for classification and regression. Neurocomputing 2(5–6):183–197. https://doi.org/10.1016/0925-2312(91)90023-5

    Article  MathSciNet  Google Scholar 

  114. Ren X, Zhao Y, Fan J, Wu H, Chen Q, Kubo T (2023) Semantic segmentation of superficial layer in intracoronary optical coherence tomography based on cropping-merging and deep learning. Infrared Phys Technol 129:104542

    Article  Google Scholar 

  115. Liu H, Liu T, Chen Y, Zhang Z, Li Y-F (2022) Ehpe: skeleton cues-based gaussian coordinate encoding for efficient human pose estimation. IEEE Trans Multimed

  116. Liu T, Wang J, Yang B, Wang X (2021) Facial expression recognition method with multi-label distribution learning for non-verbal behavior understanding in the classroom. Infrared Phys Technol 112:103594

  117. Liu T, Liu H, Yang B, Zhang Z (2023) Ldcnet: Limb direction cues-aware network for flexible human pose estimation in industrial behavioral biometrics systems. IEEE Trans Ind Inform

  118. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  119. Weiss G, Goldberg Y, Yahav E (2022) Extracting automata from recurrent neural networks using queries and counterexamples (extended version). Mach Learn pp 1–43

  120. Zhao A, Qi L, Dong J, Yu H (2018) Dual channel lstm based multi-feature extraction in gait for diagnosis of neurodegenerative diseases. Knowl-Based Syst 145:91–97. https://doi.org/10.1016/j.knosys.2018.01.004

  121. Leopold E, Kindermann J (2002) Text categorization with support vector machines. how to represent texts in input space? Mach Learn 46(1):423–444

  122. Paul J, Dutta K, Sarkar A, Das N, Roy K (2023) A survey on different feature extraction methods for writer identification and verification. Int J Appl Pattern Recog 7(2):122–144

    Article  Google Scholar 

  123. Jungo M, Wolf B, Maksai A, Musat C, Fischer A (2023) Character queries: a transformer-based approach to on-line handwritten character segmentation. In: International conference on document analysis and recognition, Springer, pp 98–114

  124. De Gregorio G, Biswas S, Souibgui MA, Bensalah A, Lladós J, Fornés A, Marcelli A (2022) A few shot multi-representation approach for n-gram spotting in historical manuscripts. In: International conference on frontiers in handwriting recognition, Springer, pp 3–17

  125. Chakraborty S, Harit G, Ghosh S (2023) Transdocanalyser: a framework for semi-structured offline handwritten documents analysis with an application to legal domain. In: International conference on document analysis and recognition, Springer, pp 45–62

  126. Barrere K, Soullard Y, Lemaitre A, Coüasnon B (2024) Training transformer architectures on few annotated data: an application to historical handwritten text recognition. Int J Doc Anal Recog (IJDAR), pp 1–14

  127. Tian X, Bu X, He L (2023) Multi-task learning with helpful word selection for lexicon-enhanced chinese ner. Appl Intell 53(16):19028–19043

    Article  Google Scholar 

  128. Agrawal V, Jagtap J, Kantipudi MVVP (2024) Exploration of advancements in handwritten document recognition techniques. Intell Syst Appl 200358. https://doi.org/10.1016/j.iswa.2024.200358

  129. Thakur U, Sharma A (2023) Offline handwritten mathematical recognition using adversarial learning and transformers. Int J Doc Anal Recog (IJDAR), pp 1–12

  130. Lee S-C, Lee D-G, Seo Y-S (2024) Determining the best feature combination through text and probabilistic feature analysis for gpt-2-based mobile app review detection. Appl Intell 54(2):1219–1246

  131. Piqueras LC, Fierro C, Lotz JF, Rust P, Rommedahl J, Due JK, Igel C, Elliott D, Pedersen CB, Salazar I et al (2022) Date recognition in historical parish records. In: International conference on frontiers in handwriting recognition, Springer, pp 49–64

  132. Schuster B, Kordon F, Mayr M, Seuret M, Jost S, Kessler J, Christlein V (2023) Multi-stage fine-tuning deep learning models improves automatic assessment of the rey-osterrieth complex figure test. In: International conference on document analysis and recognition, Springer, pp 3–19

  133. Thuon N, Du J, Zhang J (2022) Improving isolated glyph classification task for palm leaf manuscripts. In: International conference on frontiers in handwriting recognition, Springer, pp 65–79

Download references

Acknowledgements

The authors wish to thank CONAHCyT, Mexico, for funding 2021-000018-02NACF-12228 for graduate studies awarded to Loeza-Mejía.

Author information

Authors and Affiliations

  1. Artificial Intelligence Lab, National Technological, Misantla Campus, Misantla, Veracruz, Mexico

    Eddy Sánchez-DelaCruz & Cecilia-Irene Loeza-Mejía

Authors
  1. Eddy Sánchez-DelaCruz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cecilia-Irene Loeza-Mejía
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Eddy Sánchez-DelaCruz: Conceptualization of this study, Investigation, Methodology, Writing - Original draft preparation. Cecilia-Irene Loeza-Mejía: Project administration, Supervision, Formal analysis, Writing - Original draft preparation, Writing - Review & Editing.

Corresponding author

Correspondence to Cecilia-Irene Loeza-Mejía.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sánchez-DelaCruz, E., Loeza-Mejía, CI. Importance and challenges of handwriting recognition with the implementation of machine learning techniques: a survey. Appl Intell (2024). https://doi.org/10.1007/s10489-024-05487-x

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-024-05487-x

Keywords

Search

Navigation