Cited by 13Handwritten Text Recognition TechniquesVehicle License Plate RecognitionInformation Systems and Technology Applications

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Understanding contents of filled-in Bangla form images

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Integrity signals

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Crossref · 10.1007/s11042-020-09751-3

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1 of 1 references verified0 citation errors · 0 not checkable

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Citations

Cites

Research works printed but not linked: 41

  1. Rasmussen LV, Peissig PL, McCarty CA, Starren J (2011) Development of an optical character recognition pipeline for handwritten form fields from an electronic health record. J Am Med Inform Assoc 19:e90–e95 · DOI
  2. Milewski RJ, Govindaraju V, Bhardwaj A (2009) Automatic recognition of handwritten medical forms for search engines. Int J Doc Anal Recognit 11:203–218 · DOI
  3. Ghosh S, Bhattacharya R, Majhi S, et al (2018) Textual content retrieval from filled-in form images. In: Workshop on Document Analysis and Recognition. Springer, pp. 27–37 · DOI
  4. Rahal N, Tounsi M, Benjlaiel M, Alimi AM (2018) Information extraction from Arabic and Latin scanned invoices. In: 2018 IEEE 2nd international workshop on Arabic and derived script analysis and recognition (ASAR). IEEE, pp 145–150 · DOI
  5. Xue W, Li Q, Zhang Z, et al (2018) Table analysis and information extraction for medical laboratory reports. In: 2018 IEEE 16th Intl Conf on dependable, autonomic and secure computing, 16th Intl Conf on pervasive intelligence and computing, 4th Intl Conf on big data intelligence and computing and cyber science and technology congress (DASC/PiCom/DataCom/CyberSciTech. IEEE, pp 193–199 · DOI
  6. Majumder BP, Potti N, Tata S, et al (2020) Representation learning for information extraction from form-like documents. In: proceedings of the 58th annual meeting of the Association for Computational Linguistics. Pp 6495–6504 · DOI
  7. Oyedotun OK, Khashman A (2016) Document segmentation using textural features summarization and feedforward neural network. Appl Intell 45:198–212 · DOI
  8. Antonacopoulos A, Ritchings RT (1995) Representation and classification of complex-shaped printed regions using white tiles. In: proceedings of 3rd international conference on document analysis and recognition. IEEE, pp 1132–1135 · DOI
  9. Shih FY, Chen S-S (1996) Adaptive document block segmentation and classification. IEEE Trans Syst Man, Cybern Part B 26:797–802 · DOI
  10. Safonov I V, Kurilin I V, Rychagov MN, Tolstaya E V (2019) Segmentation of scanned images of newspapers and magazines. In: Document Image Processing for Scanning and Printing. Springer, pp. 107–122 · DOI
  11. Sah AK, Bhowmik S, Malakar S, et al (2018) Text and non-Text recognition using modified HOG descriptor. 2017 IEEE Calcutta Conf CALCON 2017 - Proc 2018–Janua:64–68. https://doi.org/10.1109/CALCON.2017.8280697 · DOI
  12. Bhowmik S, Sarkar R, Nasipuri M (2017) Text and non-text separation in handwritten document images using local binary pattern operator. In: Proceedings of the First International Conference on Intelligent Computing and Communication. Springer, pp. 507–515 · DOI
  13. Khan T, Mollah AF (2019) AUTNT-A component level dataset for text non-text classification and benchmarking with novel script invariant feature descriptors and D-CNN. Multimed Tools Appl 78:32159–32186 · DOI
  14. Khan T, Mollah AF (2020) Text non-text classification based on area occupancy of equidistant pixels. Procedia Comput Sci 167:1889–1900 · DOI
  15. Ghosh S, Lahiri D, Bhowmik S, Kavallieratou E, Sarkar R (2018) Text/non-text separation from handwritten document images using LBP based features: an empirical study. J Imaging 4:57 · DOI
  16. Garz A, Sablatnig R, Diem M (2011) Using local features for efficient layout analysis of ancient manuscripts. In: 2011 19th European signal processing conference. IEEE, pp 1259–1263
  17. Gobbi S, Ciolli M, La Porta N et al (2019) New tools for the classification and filtering of historical maps. ISPRS Int J Geo-Information 8:455 · DOI
  18. Kosaraju SC, Masum M, Tsaku NZ, et al (2019) DoT-net: document layout classification using texture-based CNN. In: 2019 international conference on document analysis and recognition (ICDAR). IEEE, pp 1029–1034 · DOI
  19. Bhowmik S, Sarkar R, Nasipuri M, Doermann D (2018) Text and non-text separation in offline document images: a survey. Int J Doc Anal Recognit 21:1–20 · DOI
  20. Patil U, Begum M (2012) Word level handwritten and printed text separation based on shape features. Int J Emerg Technol Adv Eng 2:590–594
  21. Koyama J, Hirose A, Kato M (2008) Local-spectrum-based distinction between handwritten and machine-printed characters. In: 2008 15th IEEE international conference on image processing. IEEE, pp 1021–1024 · DOI
  22. Neelima KB, Arulselvi S (2020) Classification of printed text and handwritten characters with neural networks. J Crit Rev 7:134–139
  23. Malakar S, Das RK, Sarkar R, Basu S, Nasipuri M (2013) Handwritten and printed word identification using gray-scale feature vector and decision tree classifier. Procedia Technol 10:831–839 · DOI
  24. Kuhnke K, Simoncini L, Kovacs-V ZM (1995) A system for machine-written and hand-written character distinction. In: proceedings of 3rd international conference on document analysis and recognition. IEEE, pp 811–814 · DOI
  25. Garlapati BM, Chalamala SR (2017) A system for handwritten and printed text classification. In: 2017 UKSim-AMSS 19th international conference on Computer Modelling & Simulation (UKSim). IEEE, pp 50–54 · DOI
  26. Sahare P, Dhok SB (2018) Separation of handwritten and machine-printed texts from Noisy documents using Contourlet transform. Arab J Sci Eng 43:8159–8177 · DOI
  27. Sahare P, Dhok SB (2019) Separation of machine-printed and handwritten texts in Noisy documents using wavelet transform. IETE Tech Rev 36:341–361 · DOI
  28. Hamrouni S, Cloppet F, Vincent N (2014) Handwritten and printed text separation: linearity and regularity assessment. In: International Conference Image Analysis and Recognition. Springer, pp. 387–394 · DOI
  29. Peng X, Setlur S, Govindaraju V, Sitaram R (2013) Handwritten text separation from annotated machine printed documents using Markov random fields. Int J Doc Anal Recognit 16:1–16 · DOI
  30. Seuret M, Liwicki M, Ingold R (2014) Pixel level handwritten and printed content discrimination in scanned documents. In: 2014 14th international conference on Frontiers in handwriting recognition. IEEE, pp 423–428 · DOI
  31. Dutly N, Slimane F, Ingold R (2019) PHTI-WS: a printed and handwritten text identification web service based on FCN and CRF post-processing. In: 2019 international conference on document analysis and recognition workshops (ICDARW). IEEE, pp 20–25 · DOI
  32. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3431–3440 · DOI · Paper page
  33. Belaïd A, Santosh KC, d’Andecy VP (2013) Handwritten and printed text separation in real document. arXiv Prepr arXiv13034614
  34. Fukushima K, Wake N (1991) Handwritten alphanumeric character recognition by the neocognitron. IEEE Trans Neural Netw 2:355–365 · DOI
  35. Koch G, Heutte L, Paquet T (2003) Numerical sequence extraction in handwritten incoming mail documents. In: Seventh international conference on document analysis and recognition, 2003. Proceedings. IEEE, pp. 369–373 · DOI
  36. Chatelain C, Heutte L, Paquet T (2004) A syntax-directed method for numerical field extraction using classifier combination. In: Ninth International Workshop on Frontiers in Handwriting Recognition. IEEE, pp. 93–98 · DOI
  37. Mandal R, Roy PP, Pal U (2012) Date field extraction in handwritten documents. In: proceedings of the 21st international conference on pattern recognition (ICPR2012). IEEE, pp 533–536
  38. Jana P, Ghosh S, Bera SK, Sarkar R (2017) Handwritten document image binarization: An adaptive K-means based approach. In: 2017 IEEE Calcutta Conference (CALCON). IEEE, pp 226–230 · DOI
  39. Haralick RM, Shanmugam K, Dinstein IH (1973). Textural features for image classification. IEEE Trans Syst Man Cybern 610–621, SMC-3 · DOI · Paper page
  40. Öztürk Ş, Akdemir B (2018) Application of feature extraction and classification methods for histopathological image using GLCM, LBP, LBGLCM, GLRLM and SFTA. Procedia Comput Sci 132:40–46 · DOI
  41. Malakar S, Sarkar R, Basu S, et al (2020). An image database of handwritten Bangla words with automatic benchmarking facilities for character segmentation algorithms. NEURAL Comput Appl · DOI

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Metadata

Authors
Bhattacharya, R., Malakar, S., Ghosh, S., Bhowmik, S., & Sarkar, R.
Affiliations
  • Jadavpur University (IN)
  • Asutosh College (IN)
Corresponding author
Malakar, Samir
Venue
Multimedia Tools and Applications
Published
2020-09-22
Volume
80
Issue
3
Pages
3529-3570
ISSN
  • 1380-7501 (ISSN-L)
  • 1573-7721
OpenAlex
W3089140941
Cited by
13
References
1
Citations by year
2025: 1 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 5 · 2020: 1
FWCI
0.97
Type
journal article
Language
en
Open access
closed
Locations
Multimedia Tools and Applications (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Vehicle License Plate Recognition
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems and Technology Applications
Keywords
Bangla script, feature engineering

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