PANDORA: A Collaborative Platform for Semiautomatic Transcription of Handwritten Police Reports
Abstract
The Police Report (PR) is one of the primary sources of information for the foundation and promotion of public security policies. Despite the existence of mobile applications for registering PRs, for multiple reasons, many police officers still register the PR in handwritten form. Registering the PR in handwriting is a challenge for collecting information, as it imposes a step of transcribing the text, which is an arduous and poorly scalable task. This paper proposes a collaborative platform, called PANDORA, which employs Machine Learning techniques to perform an initial transcription of the handwritten PRs to be modified/improved through the collaboration of multiple expert users. An evaluation with expert users and real PRs was performed.
References
Alikhademi, K., Drobina, E., Prioleau, D., Richardson, B., Purves, D., and Gilbert, J. E. (2022). A review of predictive policing from the perspective of fairness. Artif. Intell. Law, 30:1–17.
Alves, R., David, J., Braga, R., Siqueira, K., Stroele, V., Barbosa, G., da Costa, J. P., and da Silva, I. (2022). Nutrinprice: uma plataforma colaborativa para apoiar a seleção de produtos alimentícios. In SBSC, pages 9–16. SBC.
Baek, Y., Lee, B., Han, D., Yun, S., and Lee, H. (2019). Character region awareness for text detection. In IEEE CVPR, pages 9365–9374. IEEE.
Bezerra, B. L. D., Zanchettin, C., and Braga de Andrade, V. (2012). A hybrid rnn model for cursive offline handwriting recognition. In SBRN, pages 113–118.
Blohm, I., Zogaj, S., Bretschneider, U., and Leimeister, J. M. (2018). How to manage crowdsourcing platforms effectively? Calif. Manag. Rev., 60(2):122–149.
Bonaccorso, G. (2018). Machine Learning Algorithms: Popular algorithms for data science and machine learning. Packt Publishing Ltd.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q., 13(3):319–340.
Faber, A., Rehm, S., Hernandez-Mendez, A., and Matthes, F. (2018). Collectively constructing the business ecosystem: Towards crowd-based modeling for platforms and infrastructures. In ICEIS, pages 158–172.
Granell, E. and Martínez-Hinarejos, C. D. (2016). A multimodal crowdsourcing framework for transcribing historical handwritten documents. In DocEng, pages 157–163. ACM.
Granell, E., Romero, V., and Martínez-Hinarejos, C. D. (2018). Multimodality, interactivity, and crowdsourcing for document transcription. Comput. Intell., 34(2):398–419.
Khan, S. H., Rahmani, H., Shah, S. A. A., and Bennamoun, M. (2018). A Guide to Convolutional Neural Networks for Computer Vision. Morgan & Claypool Publishers.
Kopittke, A. L. W. and Ramos, M. P. (2021). O que funciona e o que não funciona para reduzir homicídios no brasil: uma revisão sistemática. Rev. Adm. Pub., 55(2):414–437.
Likforman-Sulem, L., Hanimyan, A., and Faure, C. (1995). A hough based algorithm for extracting text lines in handwritten documents. In ICDAR, volume 2, pages 774–777.
Louloudis, G., Gatos, B., Pratikakis, I., and Halatsis, C. (2009). Text line and word segmentation of handwritten documents. Pattern recognition, 42(12):3169–3183.
Lourenço, V., Mann, P., Guimaraes, A., Paes, A., and de Oliveira, D. (2018). Towards safer (smart) cities: Discovering urban crime patterns using logic-based relational machine learning. In IJCNN, pages 1–8. IEEE.
Mitchell, T. M. and Mitchell, T. M. (1997). Machine learning. McGraw-hill NY. Nikitha, A., Geetha, J., and JayaLakshmi, D. (2020). Handwritten text recognition using deep learning. In RTEICT, pages 388–392.
Plamondon, R. and Srihari, S. (2000). Online and off-line handwriting recognition: a comprehensive survey. IEEE TPAMI, 22(1):63–84.
Purohit, A. and Chauhan, S. (2016). A literature survey on handwritten character recognition. International Journal of Computer Science and Information Technology, 7:1–5.
Reis, D., Melo, A., Coelho, A. L. V., and Furtado, V. (2006). Towards optimal police patrol routes with genetic algorithms. In IEEE ISI, pages 485–491.
SEPM (2015). Instruçao normativa pmerj/emg-pm-3 no 48 de 30 de dezembro de 2015.
Silva, G. (2021). Transcrição automática de textos em português escritos a mão usando deep learning. Bachelor’s thesis, Universidade Federal Fluminense, Brasil.
Tomic, M., Grzunov, L., and Ivanovic, M. D. (2021). Crowdsourcing transcription of historical manuscripts: Citizen science as a force of revealing historical evidence from croatian glagolitic manuscripts. Educ. Inf., 37(4):443–464.
Alves, R., David, J., Braga, R., Siqueira, K., Stroele, V., Barbosa, G., da Costa, J. P., and da Silva, I. (2022). Nutrinprice: uma plataforma colaborativa para apoiar a seleção de produtos alimentícios. In SBSC, pages 9–16. SBC.
Baek, Y., Lee, B., Han, D., Yun, S., and Lee, H. (2019). Character region awareness for text detection. In IEEE CVPR, pages 9365–9374. IEEE.
Bezerra, B. L. D., Zanchettin, C., and Braga de Andrade, V. (2012). A hybrid rnn model for cursive offline handwriting recognition. In SBRN, pages 113–118.
Blohm, I., Zogaj, S., Bretschneider, U., and Leimeister, J. M. (2018). How to manage crowdsourcing platforms effectively? Calif. Manag. Rev., 60(2):122–149.
Bonaccorso, G. (2018). Machine Learning Algorithms: Popular algorithms for data science and machine learning. Packt Publishing Ltd.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q., 13(3):319–340.
Faber, A., Rehm, S., Hernandez-Mendez, A., and Matthes, F. (2018). Collectively constructing the business ecosystem: Towards crowd-based modeling for platforms and infrastructures. In ICEIS, pages 158–172.
Granell, E. and Martínez-Hinarejos, C. D. (2016). A multimodal crowdsourcing framework for transcribing historical handwritten documents. In DocEng, pages 157–163. ACM.
Granell, E., Romero, V., and Martínez-Hinarejos, C. D. (2018). Multimodality, interactivity, and crowdsourcing for document transcription. Comput. Intell., 34(2):398–419.
Khan, S. H., Rahmani, H., Shah, S. A. A., and Bennamoun, M. (2018). A Guide to Convolutional Neural Networks for Computer Vision. Morgan & Claypool Publishers.
Kopittke, A. L. W. and Ramos, M. P. (2021). O que funciona e o que não funciona para reduzir homicídios no brasil: uma revisão sistemática. Rev. Adm. Pub., 55(2):414–437.
Likforman-Sulem, L., Hanimyan, A., and Faure, C. (1995). A hough based algorithm for extracting text lines in handwritten documents. In ICDAR, volume 2, pages 774–777.
Louloudis, G., Gatos, B., Pratikakis, I., and Halatsis, C. (2009). Text line and word segmentation of handwritten documents. Pattern recognition, 42(12):3169–3183.
Lourenço, V., Mann, P., Guimaraes, A., Paes, A., and de Oliveira, D. (2018). Towards safer (smart) cities: Discovering urban crime patterns using logic-based relational machine learning. In IJCNN, pages 1–8. IEEE.
Mitchell, T. M. and Mitchell, T. M. (1997). Machine learning. McGraw-hill NY. Nikitha, A., Geetha, J., and JayaLakshmi, D. (2020). Handwritten text recognition using deep learning. In RTEICT, pages 388–392.
Plamondon, R. and Srihari, S. (2000). Online and off-line handwriting recognition: a comprehensive survey. IEEE TPAMI, 22(1):63–84.
Purohit, A. and Chauhan, S. (2016). A literature survey on handwritten character recognition. International Journal of Computer Science and Information Technology, 7:1–5.
Reis, D., Melo, A., Coelho, A. L. V., and Furtado, V. (2006). Towards optimal police patrol routes with genetic algorithms. In IEEE ISI, pages 485–491.
SEPM (2015). Instruçao normativa pmerj/emg-pm-3 no 48 de 30 de dezembro de 2015.
Silva, G. (2021). Transcrição automática de textos em português escritos a mão usando deep learning. Bachelor’s thesis, Universidade Federal Fluminense, Brasil.
Tomic, M., Grzunov, L., and Ivanovic, M. D. (2021). Crowdsourcing transcription of historical manuscripts: Citizen science as a force of revealing historical evidence from croatian glagolitic manuscripts. Educ. Inf., 37(4):443–464.
Published
2023-05-22
How to Cite
SANTOS, Wagner; COELHO, Gabriel; PAES, Aline; ROSSETI, Isabel; DE OLIVEIRA, Daniel.
PANDORA: A Collaborative Platform for Semiautomatic Transcription of Handwritten Police Reports. In: BRAZILIAN SYMPOSIUM ON COLLABORATIVE SYSTEMS (SBSC), 18. , 2023, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 44-56.
ISSN 2326-2842.
DOI: https://doi.org/10.5753/sbsc.2023.229066.
