Offline Handwritten Character Recognition Including Compound Character from Scanned Document
Asian Journal of Research in Computer Science,
Recognizing the handwritten characters and converting them into machine-editable text is very tedious due to the diversity of writing styles and character patterns. Extracting data from images and identifying the characters becomes more complicated when a language consists of compound structures and characters, such as Bengali. There has been a lack of programs for recognizing Bengali scripted basic and com-plex numeric signs and letters with high accuracy. This paper develops a novel approach to extracting and identifying Bengali handwritten primary characters, digits, and primarily used compound characters. In this proposed model, an image containing Bengali handwritten text takes as input and processed. Then processed images are segmented into lines and characters. The features are extracted from segmented characters and recognized using a Convolutional Neural Network (CNN). The CNN obtains 98.23% accuracy in the training dataset and 96.02% in the validation dataset. Apart from that, the proposed model has gained 89.6% precision and 92.6% recall scores on scanned image data.
- Character recognition
- computer vision
- image processing
- artificial intelligence
- deep learning
How to Cite
Tappert CC, Suen CY, Wakahara T. The state of the art in on-line handwriting recognition; 1990.
Rehman, Saba T. Off-line cursive script recognition: Current advances, comparisons and remaining problems. Artificial Intelligence Review. 2012;37(4): 261–288.
Choudhary. A review of various character segmentation techniques for cursive handwritten words recognition; 2014.
Hossain F, Naznin YA, Joarder Z, Islam, Uddin J. Recognition and Solution for Handwritten Equation Using Convolutional Neural Network; 2018.
Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. Nature Publishing Group. 2015;521(7553):436–444.
Sahare P, Dhok SB. Robust character segmentation and recognition schemes for multilingual indian document images. IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India). 2019;36(2):209–222.
Kumar M, Jindal MK, Sharma RK. K-nearest neighbor based offline handwritten gurmukhi character recognition pradesh, India; 2011.
Majid N, Smith EHB. Segmentation-free bangla offline handwriting recognition using sequential detection of characters and diacritics with a faster R-CNN. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR. 2019:228–233.
Chowdhury S, Wasee FR, Islam MS, Zaman HU. Bengali handwriting recognition and conversion to editable text; 2018.
Azad Rabby KMS, Haque S, Abujar S, Hossain SA. Ekushnet: Using convolutional neural network for Bangla handwritten recognition. In Procedia Computer Science. 2018;143:603–610. DOI: 10.1016/j.procs.2018.10.437
Pal U, Wakabayashi T, Kimura F. Handwritten bangla compound character recognition using gradient feature. 2008: 208–213.
Md. Rahman M, Akhand MAH, Islam S, Chandra Shill P, Hafizur Rahman MM. Bangla handwritten character recognition using convolutional neural network. International Journal of Image, Graphics and Signal Processing. 2015;7(8):42–49.
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