Recognition of Various Handwritten Indian Numerals Using Artificial Neural Network

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)

Abstract

In current years, extracting documents written by hand is extensively studied topic in image analysis and optical character recognition. These extractions of document images find their applications in document analysis, content analysis, document retrieval, and much more. Many complex text extracting processes such as maximization likelihood ratio (MLR), neural networks, edge point detection technique, corner point edge detection are generally employed for extraction of text documents from images. This article uses feed-forward propagation model of neural network for recognition of various Indian handwritten numerals like Punjabi, Hindi, Bengali, Telugu, and Marathi. Recognition is achieved by initially acquiring the image, then preprocessing it and then feature extraction. Preprocessing is performed by binarizing the image and segmenting the preprocessed image by cropping it to its edges. Feature extraction involves the normalizing the numeral matrix into 12 × 10 matrixes. Feature recognition applies artificial neural network for detection of numerals. The network is constructed with 120 input nodes, 10 hidden layer nodes, and 10 output nodes. The network has one input, single output, and a hidden layer. The numbers used for training are divided using a morphological method, and the network is trained for various Indian numerals. The proposed system has 98% recognition accuracy with respect to training data.

Keywords

Neural network OCR Multilingual documents Handwritten documents 

References

  1. 1.
    Desai, Apurva A. “Gujarati handwritten numeral optical character reorganization through neural network”, Pattern recognition 43, no. 7, pp. 2582–2589, 2010.Google Scholar
  2. 2.
    Bhattacharya, Ujjwal, and Bidyut B. Chaudhuri. “Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals.” IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 3, pp: 444–457, 2009.Google Scholar
  3. 3.
    Leo Pauly, Rahul D Raj, Dr. Binu paul, “Hand written Digit Recognition System for South Indian Languages using Artificial Neural Networks”, Contemporary Computing (IC3), 2015 Eighth International Conference on, pp. 122–126, 2015.Google Scholar
  4. 4.
    Gunjan Singh, Sushma Lehri, “Recognition of Handwritten Hindi Characters using Backpropagation Neural Network”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (4), pp. 4892–4895, 2012.Google Scholar
  5. 5.
    Sukhpreet Singh, “Optical Character Recognition Techniques: A Survey”, Journal of Emerging Trends in Computing and Information Sciences, Vol. 4, No. 6 June 2013, pp. 545–550, ISSN 2079-8407.Google Scholar
  6. 6.
    Neeraj Kumar and Sheifali Gupta, “Offline Handwritten Gurmukhi Character Recognition: A Review”, International Journal of Software Engineering and Its Applications, Vol. 10, No. 5 (2016), pp. 77–86.Google Scholar
  7. 7.
    Anwar Ali Sanjrani, Junaid Baber, Maheen Bakhtyar, Waheed Noor, Muhammad Khalid, “Handwritten Optical Character Recognition System for Sindhi Numerals”, 2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube),  https://doi.org/10.1109/icecube.2016.7495235.
  8. 8.
    Shalin A. Chopra, Amit A. Ghadge, Onkar A. Padwal, Karan S. Punjabi, Prof. Gandhali S. Gurjar, “Optical Character Recognition”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 1, January 2014, pp. 4956–4958, ISSN (Online): 2278-1021, ISSN (Print): 2319-5940.Google Scholar
  9. 9.
    Powers, David M W (2011), “Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation”, Journal of Machine Learning Technologies. 2 (1): 37–63.Google Scholar
  10. 10.
    Guriqbal Singh, Vikas Mongia, “Recognition Of Punjabi Script Character And Number For Multiple Fonts”, International Journal Of Engineering And Computer Science, ISSN: 2319–7242, Volume 4 Issue 10 Oct 2015, pp. 14594–14598.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of E.C.E., College of Engineering and TechnologyMody UniversityLakshmangarh (Sikar)India

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