Bangla Handwritten Character Recognition using Convolutional Neural Network

Rahman, M.M. Hafizur; Akhand, M. A. H.; Islam, Shahidul; Shill, Pintu Chandra; Rahman, M. M. Hafizur

doi:10.5815/ijigsp.2015.08.05

Cited by 93 publications

(25 citation statements)

References 12 publications

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“…By comparing the performance of this models with the existing works Table-7, this models perform better than most of them notably than proposed model by Rahman et al [6] which got 85.96% accuracy and slightly better than proposed method by Bhattacharya et al [10] which got 95.84% accuracy. Whereas proposed model have achieved 96.7% accuracy.…”

Section: Comparison With Existing Workmentioning

confidence: 73%

Handwritten Bangla Character Recognition using Inception Convolutional Neural Network

Taufique¹,

Rahman²,

Pranta³

et al. 2018

IJCA

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With the advancement of modern technology the necessity of pattern recognition has increased a lot. Character recognition it's part of pattern recognition. In last few decades there has been some researches on optical character recognition(OCR) for so many languages like -Roman, Japanese, African, Chinese, English and some researches of Indian language like -Tamil, Devanagari, Telugu, Gujratietc and so many other languages. There are very few works on handwritten Bangla character recognition. As it is tough to understand like Bangla language because of different people handwritten varies in fervidity or formation, stripe and angle. For this it's so much challenging to work in this field. In some researches SVM, MLP, ANN, HMM, HLP & CNN has been used for handwritten Bangla character recognition. In this paper an attempt is made to recognize handwritten Bangla character using Convolutional Neural Network along with the method of inception module without feature extraction. The feature extraction occurs during the training phase rather than the dataset preprocessing phase. As CNN can't take input data that varying in shape ,so had to rescaled the dataset images at fixed different size. In total final dataset contains 100000 images of dimension 28x28. 85000 images is used for training and 3000 images is used for testing. After analyzing the results a conclusion is derived on the proposed work and stated the future goals and plans to achieve highest success and accuracy rate.

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Section: Comparison With Existing Workmentioning

confidence: 73%

Handwritten Bangla Character Recognition using Inception Convolutional Neural Network

Taufique¹,

Rahman²,

Pranta³

et al. 2018

IJCA

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“…In [19] M. M. Rahaman et.al proposed a CNN based model. This method normalizes the written character images and then employs CNN to classify individual characters.…”

Section: Existing Work On Bengali Handwritten Numeral Recognitionmentioning

confidence: 99%

BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks

Sufian

Ghosh

Naskar

et al. 2022

Journal of King Saud University - Computer and Information Scie

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Bengali handwritten digit recognition can be done using different image classification techniques. But the images of handwritten digits are different from natural images as the orientation of a digit as well as similarity of features of different digits are important. On the other hand, deep convolutional neural networks are achieving huge success in computer vision problems, especially in image classification. This BDNet is a densely connected deep convolutional neural network model based on state-of-the-art algorithm DenseNet to classify Bengali handwritten numeral digits. The BDNet has end-to-end trained using ISI Bengali handwritten numeral dataset with 5-fold cross-validation. The BD-Net has achieved a test accuracy of 99.65%(baseline was 99.40%) on test data of ISI Bengali handwritten numerals. The trained model also gives 97.50% on own created dataset(which are not used during training). That is, this model gives a 41.66% error reduction compared to the previous state-of-the-art model. Codes, trained model and own dataset available at: https://github.com/Sufianlab/BDNet.

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“…These creators have clarified that the essential pre-processing strategies are required to beat the issues with the info or source. A plan named as format coordinating has been used for word acknowledgment in [16] with the feature ID as [22] had proposed a technique for perceiving Bangla written by hand characters utilizing the convolution neural systems. As a rule, abnormal state feature extraction is utilized for identifying and perceiving the state of a specific protest in the image.…”

Section: Related Workmentioning

confidence: 99%

A New Hybrid Algorithm for Telugu Word Retrieval and Recognition

Lakshmi¹,

Campus²,

Babu³

2018

IJIES

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Due to their many applications the optical character recognition (OCR) systems have been developed even for scripts like Telugu. Due to the huge number of symbols utilization, identifying the Telugu words are very much complicated. Pre-computed symbol features have been stored by these types of systems to be recognized or to retrieve in a database. Hence, searching of Telugu script from the database is a challenging task due to the complication in finding the features of the Telugu word images or scripts. Here, we had implemented novel Telugu script recognition and retrieval based on the extraction of texture properties features using iterative partitioned clustering (IPC) for classification of word images. In addition, the statistical feature extraction and similarity matching performance is further improved that measures the similarity between trained and test templates.For testing purpose, we utilized noisy, corrupted and occlusion scanned documents as a query input word images, also considered multi conjunct vowel consonant clustered word images. Our extensive simulation analysis shown that the proposed methodology finds most relevant word images from database even under such conditions. Our proposed scheme has performed superior to the conventional approaches presented in the literature in terms of mean Average Precision (mAP) and mean Average Recall (mAR).

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Bangla Handwritten Character Recognition using Convolutional Neural Network

Cited by 93 publications

References 12 publications

Handwritten Bangla Character Recognition using Inception Convolutional Neural Network

Handwritten Bangla Character Recognition using Inception Convolutional Neural Network

BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks

A New Hybrid Algorithm for Telugu Word Retrieval and Recognition

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