Optical character recognition using back propagation neural network

Afroge, Shyla; Ahmed, Boshir; Mahmud, Firoz

doi:10.1109/icecte.2016.7879615

Cited by 34 publications

(22 citation statements)

References 1 publication

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“…Pada penelitian Farsi Character, metode yang digunakan yaitu JST-BP , akurasi yang dihasilkan yaitu 85% dengan jumlah dataset sebanyak 250 citra dan terdapat 32 kelas sesuai dengan jumlah karakter [18]. Penelitian lainnya yaitu optical character recognition (OCR), dengan menggunakan 62 karakter dan dataset sebanyak 558 citra menghasilkan akurasi sebesar 99% untuk citra digit numerik (0~9), huruf kapital (A~Z) menghasilkan akurasi sebesar 97%, huruf kecil (a~z) menghasilkan akurasi sebesar 96% dan untuk alpanumeric character akurasi yang dihasilkan sebesar 93% [19].…”

Section: Tinjauan Pustakaunclassified

“…Aksara berfungsi sebagai media penulisan untuk membaca karya sastra kuno berbahasa daerah. Huruf sasak merupakan salah satu aksara tradisional nusantara yang digunakan oleh masyarakat suku Sasak di Lombok, Indonesia untuk menulis bahasa daerah yaitu bahasa sasak, yang terdiri dari 18 karakter dasar [19]. Semua karakter huruf sasak dapat dilihat pada Gambar 1.…”

Section: Aksara Sasakunclassified

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Pengenalan Pola Tulisan Tangan Aksara Sasak Menggunakan Metode Linear Discriminant Analysis dan Jaringan Syaraf Tiruan Jenis Backpropagation

Maharani

Bimantoro²

2020

JTIKA

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Sasak’s script is the one of Indonesia's cultural heritages which is endangered language category and it needs to be preserved so its not become extinct script. If there is no preservation, Sasak’s script will be close to extinction. Given the importance of conservation efforts, a reserch was carried out to recognize the Sasak’s script handwriting patterns. This reserch aims to see the optimal model for recognizing Sasak script patterns and to see how accurate the Linear Discriminant Analysis method as extraction features and Backpropagation Neural Network as a classification method for recognizing Sasak’s script patterns. The dataset in this reserch consisted of three variations amount of data which are 13500, 10800 and 2700. According to the final results of the study the best accuracy was obtained in the 10800 dataset, 92.20% with 92% precision and 92% recall with 250,691s computing time. The test is based on the best paramater of DCT coefficient test parameters, eigen value, number of neurons, number of hidden layers and learning rate that has been tested before.

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Section: Tinjauan Pustakaunclassified

Section: Aksara Sasakunclassified

Pengenalan Pola Tulisan Tangan Aksara Sasak Menggunakan Metode Linear Discriminant Analysis dan Jaringan Syaraf Tiruan Jenis Backpropagation

Maharani

Bimantoro²

2020

JTIKA

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“…The BP algorithm determines the weight for a multilayer ANN with feed-forward connections .The activation function used was Logsig. Optical Character Recognition using Back Propagation Neural Network [4] In research [4], they used back propagation neural network to recognize printed English characters. The feedforward network had one input, one hidden layer and one output layer.…”

Section: Application Of Neural Network In Charactermentioning

confidence: 99%

OCR for Printed Bangla Characters Using Neural Network

Isthiaq¹,

Saif²

2020

IJMECS

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Optical Character recognition is a buzzword in the field of computing. Artificial neural networks are being used to recognize characters for a long time. ANN has the ability to learn and model non-linear and complex relationships, which is really important because in real life, many of the relationships between inputs and outputs are non-linear as well as complex. Research in the field of OCR with Bangla language is not as vast as the English language. So, there is a scope of research in this area. It can be used to search and scan hundreds of Bangla documents within seconds and can easily manipulate the data. It is developed for various purpose like for vision impaired person where OCR software can help turn books, magazines and other printed documents into accessible files that they can listen. The limitation of traditional OCR are sufficient dataset is not available, all different font of characters are not available and there are lots of complex and similar shape characters for which accuracy not good. In our research, we first tried to make a dataset large enough so that we can train our neural network as they require big data to train. We built our own dataset of 2,97,898 Bangla single character images of different fonts. Then for implementing neural network we used Scikit-learn's multi-layer perceptron classifier and we also implemented our own multi-layer feed forward back propagation neural network using a machine learning framework named Tensorflow. We have also built a GUI application to demonstrate the recognition of Bangla single character images.

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“…To evaluate performance, many image databases are utilized as an entry dataset. Many recognition systems have been proposed in recent years, where the main aspect lays on higher recognition accuracy [12] - [14]. Over the past decade a ground breaking results by convolution neural network been shown in pattern recognition.…”

Section: Review Of Robotic Armmentioning

confidence: 99%

Development of Smart Number Writing Robotic Arm using Stochastic Gradient Decent Algorithm

Velrajkumar¹,

Senthilpari²,

P.³

et al. 2019

IJITEE

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Robotics and Neural Networks will play a major role in the future of manufacturing and automation process. Nowadays not many robotic systems are smart systems, in the sense that they operate on a predefined algorithm to do their task. This research focuses on a design and development of a robotic arm with a visual input. The robotic arm will perform its job with the help of visual aid. The system will analyze the input image upon which the decision to write a number using Stochastic Gradient Decent (SGD) algorithm. In a nutshell this research work shows how the neural network can be incorporated with robot arm control, which is a desired field of interest in development of smart robotic systems. This work presents where the robotic arm is incorporated together with a neural network to perform a task of writing numbers using vision

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Optical character recognition using back propagation neural network

Cited by 34 publications

References 1 publication

Pengenalan Pola Tulisan Tangan Aksara Sasak Menggunakan Metode Linear Discriminant Analysis dan Jaringan Syaraf Tiruan Jenis Backpropagation

Pengenalan Pola Tulisan Tangan Aksara Sasak Menggunakan Metode Linear Discriminant Analysis dan Jaringan Syaraf Tiruan Jenis Backpropagation

OCR for Printed Bangla Characters Using Neural Network

Development of Smart Number Writing Robotic Arm using Stochastic Gradient Decent Algorithm

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