ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

CNN-RNN Based Handwritten Text Recognition


Affiliations
1 Department of Electrical and Electronics Engineering, PSG Institute of Technology and Applied Research, India
     

   Subscribe/Renew Journal


At present most of the scripts are handwritten due to the ease of using a pen tip in place of a keyboard, hence errors are common due to illegibility of the human handwriting. To avoid this problem handwriting recognition is essential. Offline handwritten Text recognition (OHTR) has become one of the major areas of research in recent times because of the need to eliminate errors due to misinterpretation of handwritten text and the need for automation to improve efficiency. The application of this system can be seen in fields like handwritten application interpretations, postal address recognition, signature verification, and various others. In this project, offline handwritten Text recognition is performed using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) which uses the architecture of Recurrent Neural network (RNN) and Connectionist Temporal Classification (CTC). The neural network is trained and tested using the IAM database containing handwritten English text. The implementation of this work is done using image segmentation-based handwritten text recognition where OpenCV is used for performing image processing and TensorFlow is used for training and text recognition. This whole system is developed using python and the output is displayed in a word file.

Keywords

Offline Handwritten Text Recognition, Convolutional Neural Network, Recurrent Neural Network, Connectionist Temporal Classification, Long Short-Term Memory.
Subscription Login to verify subscription
User
Notifications
Font Size

  • P. Shivakumara, D. Tang, M. Asadzadehkaljahi, T. Lu, U. Pal and M. Hossein Anisi, “CNN-RNN based Method for License Plate Recognition”, CAAI Transactions on Intelligence Technology, Vol. 3, No. 3, pp. 169-175, 2018.

  • J. Sueiras, V. Ruiz, A. Sanchez and J.F. Velez, “Offline Continuous Handwriting Recognition using Sequence to Sequence Neural Networks”, Neurocomputing, Vol. 289, pp. 119-128, 2018.

  • A. Sampath and N. Gomathi, “Handwritten Optical Character Recognition by Hybrid Neural Network Training Algorithm”, The Imaging Science Journal, Vol. 67, No. 7, pp. 359-373, 2019.

  • Q. Vo, S. Kim, H. Yang and G. Lee, “Text Line Segmentation using a Fully Convolutional Network in Handwritten Document Images”, IET Image Processing, Vol. 12, No. 3, pp. 438-446, 2018.

  • J. Chung and T. Delteil, “A Computationally Efficient Pipeline Approach to Full Page Offline Handwritten Text Recognition”, Proceedings of International Conference on Document Analysis and Recognition Workshops, pp. 1-13, 2019.

  • Y. Chherawala, P. Roy and M. Cheriet, “Feature Set Evaluation for Offline Handwriting Recognition Systems: Application to the Recurrent Neural Network Model”, IEEE Transactions on Cybernetics, Vol. 46, No. 12, pp. 2825-2836, 2016.

  • Y. Weng and C. Xia, “A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices”, Mobile Networks and Applications, Vol. 25, pp. 1-22, 2019.

  • A. De Sousa Neto, B. Bezerra, A. Toselli and E. Lima, “HTR-Flor: A Deep Learning System for Offline Handwritten Text Recognition”, Proceedings of International Conference on Graphics, Patterns and Images, pp. 1-8, 2020.


Abstract Views: 258

PDF Views: 3




  • CNN-RNN Based Handwritten Text Recognition

Abstract Views: 258  |  PDF Views: 3

Authors

G. R. Hemanth
Department of Electrical and Electronics Engineering, PSG Institute of Technology and Applied Research, India
M. Jayasree
Department of Electrical and Electronics Engineering, PSG Institute of Technology and Applied Research, India
S. Keerthi Venii
Department of Electrical and Electronics Engineering, PSG Institute of Technology and Applied Research, India
P. Akshaya
Department of Electrical and Electronics Engineering, PSG Institute of Technology and Applied Research, India
R. Saranya
Department of Electrical and Electronics Engineering, PSG Institute of Technology and Applied Research, India

Abstract


At present most of the scripts are handwritten due to the ease of using a pen tip in place of a keyboard, hence errors are common due to illegibility of the human handwriting. To avoid this problem handwriting recognition is essential. Offline handwritten Text recognition (OHTR) has become one of the major areas of research in recent times because of the need to eliminate errors due to misinterpretation of handwritten text and the need for automation to improve efficiency. The application of this system can be seen in fields like handwritten application interpretations, postal address recognition, signature verification, and various others. In this project, offline handwritten Text recognition is performed using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) which uses the architecture of Recurrent Neural network (RNN) and Connectionist Temporal Classification (CTC). The neural network is trained and tested using the IAM database containing handwritten English text. The implementation of this work is done using image segmentation-based handwritten text recognition where OpenCV is used for performing image processing and TensorFlow is used for training and text recognition. This whole system is developed using python and the output is displayed in a word file.

Keywords


Offline Handwritten Text Recognition, Convolutional Neural Network, Recurrent Neural Network, Connectionist Temporal Classification, Long Short-Term Memory.

References