Search-Based Classification for Offline Tifinagh Alphabets Recognition

2020 ◽  
pp. 255-267
Author(s):  
Mohammed Erritali ◽  
Youssef Chouni ◽  
Youssef Ouadid
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Processing Time ◽  
Recognition Rate ◽  
Main Difficulty ◽  
Optical Character

The main difficulty in developing a successful optical character recognition (OCR) system lies in the confusion between the characters. In the case of Amazigh writing (Tifinagh alphabets), some characters have similarities based on rotation or scale. Most of the researchers attempted to solve this problem by combining multiple descriptors and / or classifiers which increased the recognition rate, but at the expense of processing time that becomes more prohibitive. Thus, reducing the confusion of characters and their recognition times is the major challenge of OCR systems. In this chapter, the authors present an off-line OCR system for Tifinagh characters.

scholarly journals Performance Evaluation of Automatic Number Plate Recognition on Android Smartphone Platform

2017 ◽  
Vol 7 (4) ◽  
pp. 1973
Author(s):  
Teddy Surya Gunawan ◽  
Abdul Mutholib ◽  
Mira Kartiwi
Keyword(s):  
Character Recognition ◽  
Template Matching ◽  
Optical Character Recognition ◽  
Processing Time ◽  
Intelligent System ◽  
Recognition Rate ◽  
The Other ◽  
Other Hand ◽  
Additional Processing ◽  
Artificial Neural Network Ann

<span>Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to recognize the character on vehicle number plate. Previous researches implemented ANPR system on personal computer (PC) with high resolution camera and high computational capability. On the other hand, not many researches have been conducted on the design and implementation of ANPR in smartphone platforms which has limited camera resolution and processing speed. In this paper, various steps to optimize ANPR, including pre-processing, segmentation, and optical character recognition (OCR) using artificial neural network (ANN) and template matching, were described. The proposed ANPR algorithm was based on Tesseract and Leptonica libraries. For comparison purpose, the template matching based OCR will be compared to ANN based OCR. Performance of the proposed algorithm was evaluated on the developed Malaysian number plates’ image database captured by smartphone’s camera. Results showed that the accuracy and processing time of the proposed algorithm using template matching was 97.5% and 1.13 seconds, respectively. On the other hand, the traditional algorithm using template matching only obtained 83.7% recognition rate with 0.98 second processing time. It shows that our proposed ANPR algorithm improved the recognition rate with negligible additional processing time.</span>

scholarly journals Recognition of Handwritten Characters using Deep Convolutional Neural Network

2019 ◽  
Vol 8 (6S4) ◽  
pp. 314-317
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Writing Style ◽  
Handwritten Character Recognition ◽  
New Approach ◽  
Optical Character ◽  
Handwritten Character ◽  
Handwritten Recognition ◽  
Difficult Part

Handwritten character recognition (HCR) mainly entails optical character recognition. However, HCR involves in formatting and segmentation of the input. HCR is still an active area of research due to the fact that numerous verification in writing style, shape, size to individuals. The main difficult part of Indian handwritten recognition has overlapping between characters. These overlapping shaped characters are difficult to recognize that may lead to low recognition rate. These factors also increase the complexity of handwritten character recognition. This paper proposes a new approach to identify handwritten characters for Telugu language using Deep Learning (DL). The proposed work can be enhance the recognition rate of individual characters. The proposed approach recognizes with overall accuracy is 94%.

scholarly journals APPLICATION OF ZONAL AND CURVATURE FEATURES TO NUMERALS RECOGNITION

2021 ◽  
Vol 9 (2) ◽  
pp. 7-12
Author(s):  
Binod Kumar Prasad
Keyword(s):  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Signature Verification ◽  
Optical Character ◽  
Knn Classifier ◽  
Average Recognition Rate ◽  
Distance Coding

Purpose of the study: The purpose of this work is to present an offline Optical Character Recognition system to recognise handwritten English numerals to help automation of document reading. It helps to avoid tedious and time-consuming manual typing to key in important information in a computer system to preserve it for a longer time. Methodology: This work applies Curvature Features of English numeral images by encoding them in terms of distance and slope. The finer local details of images have been extracted by using Zonal features. The feature vectors obtained from the combination of these features have been fed to the KNN classifier. The whole work has been executed using the MatLab Image Processing toolbox. Main Findings: The system produces an average recognition rate of 96.67% with K=1 whereas, with K=3, the rate increased to 97% with corresponding errors of 3.33% and 3% respectively. Out of all the ten numerals, some numerals like ‘3’ and ‘8’ have shown respectively lower recognition rates. It is because of the similarity between their structures. Applications of this study: The proposed work is related to the recognition of English numerals. The model can be used widely for recognition of any pattern like signature verification, face recognition, character or word recognition in another language under Natural Language Processing, etc. Novelty/Originality of this study: The novelty of the work lies in the process of feature extraction. Curves present in the structure of a numeral sample have been encoded based on distance and slope thereby presenting Distance features and Slope features. Vertical Delta Distance Coding (VDDC) and Horizontal Delta Distance Coding (HDDC) encode a curve from vertical and horizontal directions to reveal concavity and convexity from different angles.

Application of Mathematical Morphology on Touching or Broken Characters Processing

2010 ◽  
Vol 171-172 ◽  
pp. 73-77
Author(s):  
Ying Jie Liu ◽  
Fu Cheng You
Keyword(s):  
Image Processing ◽  
Mathematical Morphology ◽  
Digital Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Binary Image ◽  
Practical Applications ◽  
Optical Character ◽  
Individual Character

It is difficult to process touching or broken characters in practical applications on optical character recognition. For touching or broken characters, a method based on mathematical morphology of binary image is put forward in the paper. On the basis of the relative theories of digital image processing, the overall process is introduced including separation of touching characters and connection of broken characters. First of all, character image is pre-processed through smoothing and threshold segmentation in order to generate binary image of characters. Then character regions which are touching or broken are processed through different operators of mathematical morphology of binary image by different structuring elements. Thus the touching characters are separated and broken characters are connected. For higher recognition rate, further processes are done to achieve normal and individual character regions.

scholarly journals Overall Equipment Utilisation (OEU) Monitoring and Remote Quality Check in Legacy Machine with Raspberry Pi

2021 ◽  
Vol 1 (2) ◽  
pp. 135-144
Author(s):  
Siti Nurul Huda Abd Rahim ◽  
Abd Halim Embong
Keyword(s):  
Neural Network ◽  
Graphical User Interface ◽  
Error Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Raspberry Pi ◽  
Manufacturing Companies ◽  
Optical Character ◽  
Quality Check

Overall Equipment Utilisation (OEU) plays an important role as a benchmark for manufacturing companies to determine each machine's efficiency. Currently, there is no proper OEU measurement system in legacy machines and only relies on human observation. This project aims to develop a measurement of OEU system by using Optical Character Recognition (OCR). An efficient Optical Character Recognition (OCR) algorithm is needed to have a high percentage of recognition rate. The outcome of this project will be a Graphical User Interface (GUI) that display real-time OEU monitoring and remote quality check for legacy machines. Pytesseract-OCR Version 4 classifier using the Recurrent Neural Network (RNN) method has been proposed in this paper. Furthermore, an error detection feature is designed from OCR output.

Optical Character Recognition System for Nastalique Urdu-Like Script Languages Using Supervised Learning

2019 ◽  
Vol 33 (10) ◽  
pp. 1953004 ◽  
Author(s):  
S. S. R. Rizvi ◽  
A. Sagheer ◽  
K. Adnan ◽  
A. Muhammad
Keyword(s):  
Supervised Learning ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Complex Structure ◽  
Recognition Rate ◽  
Digital Text ◽  
Memory Space ◽  
Optical Character ◽  
Digital Format ◽  
Printed Text

There are two main techniques to convert written or printed text into digital format. The first technique is to create an image of written/printed text, but images are large in size so they require huge memory space to store, as well as text in image form cannot be undergo further processes like edit, search, copy, etc. The second technique is to use an Optical Character Recognition (OCR) system. OCR’s can read documents and convert manual text documents into digital text and this digital text can be processed to extract knowledge. A huge amount of Urdu language’s data is available in handwritten or in printed form that needs to be converted into digital format for knowledge acquisition. Highly cursive, complex structure, bi-directionality, and compound in nature, etc. make the Urdu language too complex to obtain accurate OCR results. In this study, supervised learning-based OCR system is proposed for Nastalique Urdu language. The proposed system evaluations under a variety of experimental settings apprehend 98.4% training results and 97.3% test results, which is the highest recognition rate ever achieved by any Urdu language OCR system. The proposed system is simple to implement especially in software front of OCR system also the proposed technique is useful for printed text as well as handwritten text and it will help in developing more accurate Urdu OCR’s software systems in the future.

Deep Extreme Learning Machine-Based Optical Character Recognition System for Nastalique Urdu-Like Script Languages

2020 ◽  
Author(s):  
Syed Saqib Raza Rizvi ◽  
Muhammad Adnan Khan ◽  
Sagheer Abbas ◽  
Muhammad Asadullah ◽  
Nida Anwer ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Software Systems ◽  
Script Language ◽  
Optical Character ◽  
Handwritten Text ◽  
Learning Machine

Abstract Optical character recognition systems convert printed or handwritten scripts into digital text formats like ASCII or UNICODE. Urdu-like script languages like Urdu, Punjabi and Sindhi are widely spoken languages of the world, especially in Asia. An enormous amount of printed and handwritten text of such languages exist, which needs to be converted into computer-understandable formats for knowledge extraction. In this study, extreme learning machine’s (ELM’s) most recently proposed variant called deep extreme learning machine (DELM)-based optical character recognition (OCR) system is proposed to enhance Urdu-like script language’s character recognition rate. The proposed DELM-based character recognition model is optimizing the OCR process by reducing the overhead of Pre-processing, Segmentation and Feature Extraction Layer. The proposed system evaluations accomplished 98.75% training accuracy with 1.492 × 10−3 RMSE and 98.12% testing accuracy with 1.587 × 10−3 RMSE, with six DELM hidden layers. The results show that the proposed system has attained the foremost recognition rate as compared to any previously proposed Urdu-like script language OCR system. This technique is applicable for machine-printed text and fractionally useful for handwritten text as well. This study will aid in the advancement of more accurate Urdu-like script OCR’s software systems in the future.

scholarly journals Printed Persian Subword Recognition Using Wavelet Packet Descriptors

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Samira Nasrollahi ◽  
Afshin Ebrahimi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Text Documents ◽  
New Approach ◽  
Feature Vectors ◽  
Invariant Features ◽  
Optical Character ◽  
The Mean

In this paper, we present a new approach to offline OCR (optical character recognition) for printed Persian subwords using wavelet packet transform. The proposed algorithm is used to extract font invariant and size invariant features from 87804 subwords of 4 fonts and 3 sizes. The feature vectors are compressed using PCA. The obtained feature vectors yield a pictorial dictionary for which an entry is the mean of each group that consists of the same subword with 4 fonts in 3 sizes. The sets of these features are congregated by combining them with the dot features for the recognition of printed Persian subwords. To evaluate the feature extraction results, this algorithm was tested on a set of 2000 subwords in printed Persian text documents. An encouraging recognition rate of 97.9% is got at subword level recognition.

scholarly journals Moment invariant-based features for Jawi character recognition

2019 ◽  
Vol 9 (3) ◽  
pp. 1711 ◽  
Author(s):  
Fitri Arnia ◽  
Khairun Saddami ◽  
Khairul Munadi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Superior Performance ◽  
Support Vector ◽  
Unique Combination ◽  
Moment Invariant ◽  
Optical Character ◽  
Arabic Characters ◽  
Ancient Manuscripts

<p>Ancient manuscripts written in Malay-Arabic characters, which are known as "Jawi" characters, are mostly found in Malay world. Nowadays, many of the manuscripts have been digitalized. Unlike Roman letters, there is no optical character recognition (OCR) software for Jawi characters. This article proposes a new algorithm for Jawi character recognition based on Hu’s moment as an invariant feature that we call the tree root (TR) algorithm. The TR algorithm allows every Jawi character to have a unique combination of moment. Seven values of the Hu’s moment are calculated from all Jawi characters, which consist of 36 isolated, 27 initial, 27 middle, and 35 end characters; this makes a total of 125 characters. The TR algorithm was then applied to recognize these characters. To assess the TR algorithm, five characters that had been rotated to 90o and 180o and scaled with factors of 0.5 and 2 were used. Overall, the recognition rate of the TR algorithm was 90.4%; 113 out of 125 characters have a unique combination of moment values, while testing on rotated and scaled characters achieved 82.14% recognition rate. The proposed method showed a superior performance compared with the Support Vector Machine and Euclidian Distance as classifier.</p>

scholarly journals A Design of a Hybrid Algorithm for Optical Character Recognition of Online Hand-Written Arabic Alphabets

2019 ◽  
pp. 2067-2079
Author(s):  
Waleed Noori Hussein ◽  
Haider N. Hussain
Keyword(s):  
Decision Tree ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Optical Character ◽  
Recognition Systems ◽  
The Difference ◽  
Artificial Neural Network Ann ◽  
Handwritten Recognition

     The growing relevance of printed and digitalized hand-written characters has necessitated the need for convalescent automatic recognition of characters in Optical Character Recognition (OCR). Among the handwritten characters, Arabic is one of those with special attention due to its distinctive nature, and the inherent challenges in its recognition systems. This distinctiveness of Arabic characters, with the difference in personal writing styles and proficiency, are complicating the effectiveness of its online handwritten recognition systems. This research, based on limitations and scope of previous related studies, studied the recognition of Arabic isolated characters through the identification of its features and dots in view of producing an efficient online Arabic handwriting isolated character recognition system. It proposes a hybrid of decision tree and Artificial Neural Network (ANN), as against being combined with other algorithms as found in previous studies. The proposed recognition process has four main steps with associated sub-steps. The results showed that the proposed method achieved the highest performance at 96.7%, whereas the benchmark methods which are EDMS and Naeimizaghiani had 68.88% and 78.5 % respectively. Based on this, ANN has the best performance recognition rate at 98.8%, while the best rate for decision tree was obtained at 97.2%.

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