Text Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Hindi Handwritten Characters


  • Abhishek Mehta Department of Computer and Informative Science, Sabarmati University, Ahmadabad, Gujarat, India
  • Subhashchandra Desai Department of Computer and Informative Science, Sabarmati University, Ahmadabad, Gujarat, India
  • Ashish Chaturvedi Department of Computer and Informative Science, Sabarmati University, Formerly Calorx Teachers' University, Ahmadabad, Gujarat, India




object detection, Faster R-CNN, skip pooling, guided anchor RPN


Significant learning is at present the standard system for object disclosure. Speedier territory based convolutional neural association (Faster R-CNN) has a basic circumstance in significant learning. It has stunning area impacts in standard scenes. Regardless, under unprecedented conditions, there can even now be inadmissible acknowledgment execution, for instance, the thing having issues like hindrance, contorting, or little size. This paper proposes a novel and improved estimation reliant on the Faster R-CNN framework got together with the Faster R-CNN figuring with skip pooling and mix of consistent information. This computation can improve the revelation execution under uncommon conditions dependent on Faster R-CNN. The improvement basically has three segments: The underlying portion adds a setting information incorporate extraction model after the conv5_3 of the convolutional layer; the resulting part adds skip pooling so the past can totally secure the coherent information of the article, especially for conditions where the thing is hindered and distorted; and the third part replaces the area recommendation association (RPN) with a more capable guided anchor RPN (GA-RPN), which can keep up the survey rate while improving the revelation execution. The last can get more positive information from different segment layers of the significant neural association figuring, and is especially centered around scenes with little articles. Differentiated and Faster R-CNN, you simply look once plan, (for instance, YOLOv3), single shot pointer, (for instance, SSD512), and other article revelation computations, the estimation proposed in this paper has an ordinary improvement of 6.857% on the mean typical precision (mAP) appraisal list while keeping up a particular audit rate. This unequivocally exhibits that the proposed methodology has higher ID rate and disclosure efficiency for this circumstance.


Download data is not yet available.

Plum Analytics





Li, C. Dangerous Posture Monitoring for Undersea Diver Based on Frame Difference Method. J. Coast. Res. 2020, 103, 939-942. https://doi.org/10.2112/SI103-195.1

Aronniemi, M.; Sainio, J.; Lahtinen, J. Chemical state quantification of iron and chromium oxides using XPS: The effect of the background subtraction method. Surf. Sci. 2005, 578, 108-123. https://doi.org/10.1016/j.susc.2005.01.019

Dougherty, L.; Asmuth, J.; Blom, A.; Axel, L.; Kumar, R. Validation of an optical flow method for tag displacement estimation. IEEE Trans. Med. Imaging 1999, 18, 359-363. https://doi.org/10.1109/42.768845

Shi, D.; Zheng, L.; Liu, J. Advanced Hough Transform Using A Multilayer Fractional Fourier Method. IEEE Trans. Image Process. 2010, 19, 1558-1566. https://doi.org/10.1109/TIP.2010.2042102

Song, Y.-S.; Cho, S.-B.; Suh, I.H. Activity-Object Bayesian Networks for Detecting Occluded Objects in Uncertain Indoor Environment. In Proceedings of the Knowledge-Based Intelligent Information & Engineering Systems, International Conference, Kes, Melbourne, Australia, 14-16 September 2005. https://doi.org/10.1007/11553939_132

Shlezinger, N.; Farsad, N.; Eldar, Y.C.; Goldsmith, A.J. ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection. IEEE Trans. Wirel. Commun. 2020, 19, 3319-3331. https://doi.org/10.1109/TWC.2020.2972352

Piotr, D.; Wojek, C.; Schiele, B.; Perona, P. Pedestrian Detection: The State of the Art. IEEE Trans. Softw. Eng. 2011, 34, 743-761. https://doi.org/10.1109/TPAMI.2011.155

Viola, P.; Jones, M.J. Robust Real-Time Face Detection. Int. J. Comput. Vis. 2004, 57, 137-154. https://doi.org/10.1023/B:VISI.0000013087.49260.fb

Chen, B.-H.; Huang, S.-C. An Advanced Moving Object Detection Algorithm for Automatic Traffic Monitoring in Real-World Limited Bandwidth Networks. IEEE Trans. Multimedia 2014, 16, 837-847. https://doi.org/10.1109/TMM.2014.2298377

Zhang, J.; Wang, F.-Y.; Wang, K.; Lin, W.-H.; Xu, X.; Chen, C. Data-Driven Intelligent Transportation Systems. IEEE Trans. Intell. Transp. Syst. 2011, 12, 1624-1639. https://doi.org/10.1109/TITS.2011.2158001

Hua, X.; Wang, X.-Q.; Wang, D.; Huang, J.; Hu, X. Military Object Real-Time Detection Technology Combined with Visual Salience and Psychology. Electronics 2018, 7, 216. https://doi.org/10.3390/electronics7100216

Zhu, W.; Huang, W.; Lin, Z.; Yang, Y.; Huang, S.; Zhou, J. Data and feature mixed ensemble based extreme learning machine for medical object detection and segmentation. Multimed. Tools Appl. 2015, 75, 2815-2837. https://doi.org/10.1007/s11042-015-2582-9

Kanezaki, A.; Rodolà, E.; Cremers, D.; Harada, T. Learning Similarities for Rigid and Non-rigid Object Detection. In Proceedings of the 2014 2nd International Conference on 3D Vision, Tokyo, Japan, 8-11 December 2014; IEEE: Piscataway, NJ, USA, 2014; Volume 1, pp. 720-727. https://doi.org/10.1109/3DV.2014.61

Wang, J.; Chen, K.; Yang, S.; Loy, C.C.; Lin, D. Region Proposal by Guided Anchoring. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 16-20 June 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 2960-2969. https://doi.org/10.1109/CVPR.2019.00308

Kulkarni, A.; Callan, J.; Selective, S. Efficient and Effective Search of Large Textual Collections. ACM Trans. Inf. Syst. 2015, 33, 17.1-17.33. https://doi.org/10.1145/2738035

Wang, X.; Xiao, T.; Jiang, Y.; Shao, S.; Sun, J.; Shen, C. Repulsion Loss: Detecting Pedestrians in a Crowd. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018, Salt Lake City, UT, USA, 18-23 June 2018; pp. 7774-7783. https://doi.org/10.1109/CVPR.2018.00811

Bell, S.; Zitnick, C.L.; Bala, K.; Girshick, R. Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. arXiv 2015, arXiv:1512.04143. https://doi.org/10.1109/CVPR.2016.314

Cheng, G.; Han, J.; Zhou, P.; Xu, D. Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection. IEEE Trans. Image Process. 2019, 28, 265-278. https://doi.org/10.1109/TIP.2018.2867198

Fan, D.-P.; Ji, G.-P.; Sun, G.; Cheng, M.-M.; Shen, J.; Shao, L. Camouflaged Object Detection. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13-19 June 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 2774-2784. https://doi.org/10.1109/CVPR42600.2020.00285

Girshick, R.; Donahue, J.; Darrell, T.; Malik, J.; Malik, J. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 24-27 June 2014. https://doi.org/10.1109/CVPR.2014.81

He, K.; Zhang, X.; Ren, S.; Sun, J. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. In European Conference on Computer Vision; Springer: Cham, Switzerland, 2014. https://doi.org/10.1007/978-3-319-10578-9_23

Girshick, R. Fast R-CNN. In Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 7-13 December 2015. https://doi.org/10.1109/ICCV.2015.169

R. Rachmat and S. Suhartono, "Comparative Analysis of Single Exponential Smoothing and Holt's Method for Quality of Hospital Services Forecasting in General Hospital", Bulletin of Comp. Sci. & Electr. Eng., vol. 1, no. 2, pp. 80-86, Aug. 2020. https://doi.org/10.25008/bcsee.v1i2.8



How to Cite

Mehta, A., Desai, S., & Chaturvedi, A. . (2021). Text Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Hindi Handwritten Characters . International Journal of Advances in Data and Information Systems, 2(1). https://doi.org/10.25008/ijadis.v2i1.1197
Abstract views : 37 times