scholarly journals Recognition of Pashto Handwritten Characters Based on Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5884
Author(s):  
Muhammad Sadiq Amin ◽  
Siddiqui Muhammad Yasir ◽  
Hyunsik Ahn
Keyword(s):  
Character Recognition ◽  
Test Accuracy ◽  
Forensic Evidence ◽  
Handwritten Character Recognition ◽  
Data Set ◽  
Research Attention ◽  
Handwritten Character ◽  
Proposed Model ◽  
Strong Candidate ◽  
Processing Techniques

Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.

scholarly journals Devanagari Handwritten Character Recognition Using Deep Learning

2022 ◽  
Vol 10 (1) ◽  
pp. 102-105
Author(s):  
Kannuru Padmaja
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Automation System ◽  
Handwritten Character Recognition ◽  
Image Processing Techniques ◽  
Handwritten Character ◽  
Processing Techniques ◽  
Deep Learning Model

Abstract: In this paper, we present the implementation of Devanagari handwritten character recognition using deep learning. Hand written character recognition gaining more importance due to its major contribution in automation system. Devanagari script is one of various languages script in India. It consists of 12 vowels and 36 consonants. Here we implemented the deep learning model to recognize the characters. The character recognition mainly five steps: pre-processing, segmentation, feature extraction, prediction, post-processing. The model will use convolutional neural network to train the model and image processing techniques to use the character recognition and predict the accuracy of rcognition. Keywords: convolutional neural network, character recognition, Devanagari script, deep learning.

scholarly journals Neural Network based Handwritten Character Recognition system

2018 ◽  
Vol 7 (03) ◽  
pp. 23761-23768 ◽  
Author(s):  
Savitha Attigeri
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Character Recognition ◽  
Recognition System ◽  
Feed Forward Neural Network ◽  
Handwritten Character Recognition ◽  
Data Set ◽  
Research Areas ◽  
Handwritten Character ◽  
The Neural Network

Handwritten character recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognize handwritten characters for English alphabets without feature extraction using multilayer Feed Forward neural network. Each character data set contains 26 alphabets. Fifty different character data sets are used for training the neural network. The trained network is used for classification and recognition. In the proposed system, each character is resized into 30x20 pixels, which is directly subjected to training. That is, each resized character has 600 pixels and these pixels are taken as features for training the neural network. The results show that the proposed system yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition

scholarly journals Recognition of Offline Gujarati Handwritten Disjoint Consonants using Pattern Matching

2019 ◽  
Vol 8 (3) ◽  
pp. 7849-7851
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Good Accuracy ◽  
Recognition System ◽  
Handwritten Character Recognition ◽  
Data Set ◽  
Handwritten Character ◽  
Key Factor ◽  
Total Data ◽  
Printed Text

Image processing is one of the most popular field nowadays. Recognition of the offline isolated handwritten characters is an area which got lot of attention within the field of Image Processing. Various techniques have been proposed in the area of online and offline handwritten character recognition (HCR). In future HCR is the key factor for the transformation of written or printed text into system understandable format. Thus, providing a boost to digitization era. Gujarati is one such language which has many challenges in creation of an accurate OCR. Researchers have achieved good accuracy in the field of online Gujarati handwritten character recognition. This paper introduces a pattern recognition system which is able to recognize isolated offline Gujarati handwritten characters with higher accuracy. Experiments have been done on sub set of ( g, `, l, x, h ) consonants using a total data set of 6750 handwritten consonants by different individuals. The experimental results achieved a markable contribution in the field of handwritten character recognition.

AN ANALYTICAL STUDY OF HANDWRITTEN CHARACTER RECOGNITION

2016 ◽  
Vol 2 (4) ◽  
pp. 26
Author(s):  
VOHRA UJWAL SINGH ◽  
DWIVEDI SHRI PRAKASH ◽  
MANDORIA H.L ◽  
◽  
◽  
...  
Keyword(s):  
Character Recognition ◽  
Analytical Study ◽  
Handwritten Character Recognition ◽  
Handwritten Character

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

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