Abstract
Optical Character Recognition (OCR) has been deployed in the past in different application areas such as automatic transcription and indexing of document images, reading aid for the visually impaired persons, postal automation etc. However, the performance in many cases has not been impressive due to the fact that character segmentation is itself an error-prone and difficult operation, which leads to the poor performance of the system due to erroneous segmentation of characters. Hence, for many applications (like document indexing, Postal automation) where full character-wise transcription is not required, word recognition is the preferred method these days. This article investigates recognition of Bengali place names as word images using 5 different traditional architectures. Experiments on word images (of Bengali place names) from 608 classes were conducted. Encouraging results were obtained in all instances.
The data-set will be available on request. Please contact the first author via email.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
A sign which used in consonant alphabet of the script
References
Poznanski, A., Wolf, L.: CNN-N-gram for handwriting word recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2305–2314 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556 (2014). http://arxiv.org/abs/1409.1556
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR, abs/1512.03385 (2015). http://arxiv.org/abs/1512.03385
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. CoRR, abs/1512.00567 (2015). http://arxiv.org/abs/1512.00567
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. CoRR, abs/1610.02357 (2016). http://arxiv.org/abs/1610.02357
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenet v2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018). https://arxiv.org/pdf/1801.04381.pdf
Sharma, A., Pramod Sankar, K.: Adapting off-the-shelf CNNs for word spotting & recognition. In: 13th International Conference on Document Analysis and Recognition, ICDAR 2015, Nancy, France, August 23–26, 2015, pp. 986–990 (2015)
Sebastian Sudholt and Gernot A. Fink. Phocnet: A deep convolutional neural network for word spotting in handwritten documents. In 15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016, Shenzhen, China, October 23–26, 2016, pages 277–282, 2016
Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. In Koller, D., Schuurmans, D., Bengio, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 21, pp. 545–552 (2009)
Bluche, T., et al.: Preparatory KWS experiments for large-scale indexing of a vast medieval manuscript collection in the HIMANIS project. In: 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017, Kyoto, Japan, 9–15 November, 2017, pp. 311–316 (2017)
Chanda, S., Okafor, E., Hamel, S., Stutzmann, D., Schomaker, L.: Deep learning for classification and as tapped-feature generator in medieval word-image recognition. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 217–222 (2018)
Chakrapani Gv, A., Chanda, S., Pal, U., Doermann, D.: One-shot learning-based handwritten word recognition. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W.Q. (eds.) ACPR 2019. LNCS, vol. 12047, pp. 210–223. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-41299-9_17
Chanda, S., Baas, J., Haitink, D., Hamel, S., Stutzmann, D., Schomaker, L.: Zero-shot learning based approach for medieval word recognition using deep-learned features. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 345–350. IEEE (2018)
Stutzmann, D., et al.: Handwritten text recognition, keyword indexing, and plain text search in medieval manuscripts (2018)
Russakovsky, O., Deng, J., Hao, S., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)
Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Liu, S., Long, Yu., Zhang, D.: An efficient method for high-speed railway dropper fault detection based on depthwise separable convolution. IEEE Access 7, 135678–135688 (2019)
Yoo, B., Choi, Y., Choi, H.: Fast depthwise separable convolution for embedded systems. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11307, pp. 656–665. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04239-4_59
Pal, U., Roy, R.K., Kimura, F.: Multi-lingual city name recognition for Indian postal automation. In: 2012 International Conference on Frontiers in Handwriting Recognition, ICFHR 2012, Bari, Italy, 18–20 September, 2012, pp. 169–173. IEEE Computer Society (2012). https://doi.org/10.1109/ICFHR.2012.238
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Prasad, P.K., Banerjee, P., Chanda, S., Pal, U. (2021). Bengali Place Name Recognition - Comparative Analysis Using Different CNN Architectures. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_29
Download citation
DOI: https://doi.org/10.1007/978-981-16-1092-9_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1091-2
Online ISBN: 978-981-16-1092-9
eBook Packages: Computer ScienceComputer Science (R0)