Handwritten Bangla numeral recognition using deep long short term memory
Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. Bangla is a major language in Indian subcontinent and is the first language of Bangladesh; but unfortunately, study regarding handwritten Bangla numeral recognition (HBNR) is very...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc. ( IEEE)
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/54935/1/54935_Handwritten%20Bangla%20numeral%20recognition.pdf http://irep.iium.edu.my/54935/2/54935_Handwritten%20Bangla%20numeral%20recognition_SCOPUS.pdf http://irep.iium.edu.my/54935/ http://ieeexplore.ieee.org/document/7814922/ |
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| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English English |
| Summary: | Recognition of handwritten numerals has gained much interest in recent years due to its various application
potentials. Bangla is a major language in Indian subcontinent and is the first language of Bangladesh; but unfortunately, study regarding handwritten Bangla numeral recognition (HBNR) is very few with respect to other major languages such as English, Roman etc. Some noteworthy research works have been conducted for recognition of Bangla handwritten numeral using artificial neural network (ANN) as ANN and its various updated models are found efficient for classification task. The aim of this
study is to develop a better Bangla handwritten numeral
recognition system and hence investigated deep architecture of Long Short Term Memory (LSTM) method. LSTM is a variant
of recurrent neural networks (RNN) and is applied efficiently for image classification with its distinct features. The proposed HBNR-LSTM normalizes the written numeral images first and then employs two layers of LSTM to classify individual numerals. Unlike other methods, it does not employ any feature extraction technique. Benchmark dataset with 22000 hand written numerals with different shapes, sizes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed other prominent exiting methods. |
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