In this paper, we present an off line Tifinaghe characters recognition system. Texts are scanned using a flatbed scanner. Digitized text are normalised, noise is reduced using a median filter, baseline skew is corrected by the use of the Hough transform, and text is segmented into line and lines into words. Features are extracted using the Walsh Transformation. Finally characters are recognized by a multilayer neural network.
A MULTI-STREAM HMM APPROACH TO OFFLINE HANDWRITTEN ARABIC WORD RECOGNITIONijnlc
This document presents a multi-stream HMM approach for offline handwritten Arabic word recognition. It extracts two sets of features from each word using a sliding window approach and VH2D projection approach. These features are input to separate HMM classifiers, and the outputs are combined in a multi-stream HMM to provide more reliable recognition. The system is evaluated on 200 words, achieving a recognition rate of 83.8% using the multi-stream approach compared to 78.2% and 76.6% for the individual classifiers.
In This paper we presented new approach for cursive Arabic text recognition system. The objective is to propose methodology analytical offline recognition of handwritten Arabic for rapid implementation.The first part in the writing recognition system is the preprocessing phase is the preprocessing phase to prepare the data was introduces and extracts a set of simple statistical features by two methods : from a window which is sliding long that text line the right to left and the approach VH2D (consists in projecting every character on the abscissa, on the ordinate and the diagonals 45° and 135°) . It then injects the resulting feature vectors to Hidden Markov Model (HMM) and combined the two HMM by multi-stream approach.
Recognition of Persian handwritten characters has been considered as a significant field of research for
the last few years under pattern analysing technique. In this paper, a new approach for robust handwritten
Persian numerals recognition using strong feature set and a classifier fusion method is scrutinized to
increase the recognition percentage. For implementing the classifier fusion technique, we have considered
k nearest neighbour (KNN), linear classifier (LC) and support vector machine (SVM) classifiers. The
innovation of this tactic is to attain better precision with few features using classifier fusion method. For
evaluation of the proposed method we considered a Persian numerals database with 20,000 handwritten
samples. Spending 15,000 samples for training stage, we verified our technique on other 5,000 samples,
and the correct recognition ratio achievedapproximately 99.90%. Additional, we got 99.97% exactness
using four-fold cross validation procedure on 20,000 databases.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Pattern Recognition of Japanese Alphabet Katakana Using Airy Zeta FunctionEditor IJCATR
Character recognition is one of common pattern recognition study. There are many object used in pattern recognition, such
as Japanese alphabet character, which is a very complex character compared to common Roman character. This research focus on
pattern recognition of Japanese character handwriting, Katakana. The pattern recognition process of a letter of the alphabet uses Airy
Zeta Function, with its input file is a .bmp file. User can write directly on an input device of the system. The testing of the system
examines 460 letter characters. The first testing that examines 230 characters result in an accuracy of 55,65%, whilst the second testing
that examines 460 characters produces an accuracy of 64,56% in recognizing the letters. These accuracy are much determined by the
quantity of training. The approach of pattern recognition is a statistical approach, where more pattern of letters are trained and saved as
a reference, more intelligent the system . The implementation of Airy zeta function methods in recognizing Japanese letter pattern is
able to produce high accuracy level.
IRJET- Classification of Hindi Maatras by Encoding SchemeIRJET Journal
This document presents a novel encoding scheme for classifying Hindi modifiers or matras. It describes existing techniques for modifier segmentation that use projection profiles and character heights. The proposed method segments modifiers using three encoding levels to assign a distinctive code for each modifier. For ascenders, the codes are based on the middle portion, skeletonized shape, and ends. For descenders, the codes consider width, last pixel, and lower right space. The method was tested on printed and handwritten modifiers, achieving over 90% accuracy for both ascenders and descenders. The encoding approach allows for direct classification of modifiers without feature extraction.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against developing mental illness and improve symptoms for those who already suffer from conditions like anxiety and depression.
A MULTI-STREAM HMM APPROACH TO OFFLINE HANDWRITTEN ARABIC WORD RECOGNITIONijnlc
This document presents a multi-stream HMM approach for offline handwritten Arabic word recognition. It extracts two sets of features from each word using a sliding window approach and VH2D projection approach. These features are input to separate HMM classifiers, and the outputs are combined in a multi-stream HMM to provide more reliable recognition. The system is evaluated on 200 words, achieving a recognition rate of 83.8% using the multi-stream approach compared to 78.2% and 76.6% for the individual classifiers.
In This paper we presented new approach for cursive Arabic text recognition system. The objective is to propose methodology analytical offline recognition of handwritten Arabic for rapid implementation.The first part in the writing recognition system is the preprocessing phase is the preprocessing phase to prepare the data was introduces and extracts a set of simple statistical features by two methods : from a window which is sliding long that text line the right to left and the approach VH2D (consists in projecting every character on the abscissa, on the ordinate and the diagonals 45° and 135°) . It then injects the resulting feature vectors to Hidden Markov Model (HMM) and combined the two HMM by multi-stream approach.
Recognition of Persian handwritten characters has been considered as a significant field of research for
the last few years under pattern analysing technique. In this paper, a new approach for robust handwritten
Persian numerals recognition using strong feature set and a classifier fusion method is scrutinized to
increase the recognition percentage. For implementing the classifier fusion technique, we have considered
k nearest neighbour (KNN), linear classifier (LC) and support vector machine (SVM) classifiers. The
innovation of this tactic is to attain better precision with few features using classifier fusion method. For
evaluation of the proposed method we considered a Persian numerals database with 20,000 handwritten
samples. Spending 15,000 samples for training stage, we verified our technique on other 5,000 samples,
and the correct recognition ratio achievedapproximately 99.90%. Additional, we got 99.97% exactness
using four-fold cross validation procedure on 20,000 databases.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Pattern Recognition of Japanese Alphabet Katakana Using Airy Zeta FunctionEditor IJCATR
Character recognition is one of common pattern recognition study. There are many object used in pattern recognition, such
as Japanese alphabet character, which is a very complex character compared to common Roman character. This research focus on
pattern recognition of Japanese character handwriting, Katakana. The pattern recognition process of a letter of the alphabet uses Airy
Zeta Function, with its input file is a .bmp file. User can write directly on an input device of the system. The testing of the system
examines 460 letter characters. The first testing that examines 230 characters result in an accuracy of 55,65%, whilst the second testing
that examines 460 characters produces an accuracy of 64,56% in recognizing the letters. These accuracy are much determined by the
quantity of training. The approach of pattern recognition is a statistical approach, where more pattern of letters are trained and saved as
a reference, more intelligent the system . The implementation of Airy zeta function methods in recognizing Japanese letter pattern is
able to produce high accuracy level.
IRJET- Classification of Hindi Maatras by Encoding SchemeIRJET Journal
This document presents a novel encoding scheme for classifying Hindi modifiers or matras. It describes existing techniques for modifier segmentation that use projection profiles and character heights. The proposed method segments modifiers using three encoding levels to assign a distinctive code for each modifier. For ascenders, the codes are based on the middle portion, skeletonized shape, and ends. For descenders, the codes consider width, last pixel, and lower right space. The method was tested on printed and handwritten modifiers, achieving over 90% accuracy for both ascenders and descenders. The encoding approach allows for direct classification of modifiers without feature extraction.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against developing mental illness and improve symptoms for those who already suffer from conditions like anxiety and depression.
El documento resume una inspección realizada a una construcción de cerramientos en el Urbanismo Hugo Chavez Frias en el estado Lara de Venezuela. Se detalla que los trabajos no han podido concluirse debido a la falta de materiales. Una posible solución sería permitir que la empresa contratista suministre los materiales faltantes para continuar y completar la construcción, la cual beneficiará a 260 familias con apartamentos. El documento incluye fotos que muestran el estado de la construcción y planos eléctricos y de plomería.
El documento presenta el marco metodológico de una investigación sobre metodología de la investigación en ingeniería de sistemas. Describe los elementos clave del marco metodológico como la modalidad y tipo de investigación, población y muestra, técnicas de recolección y análisis de datos, y las fases y pasos lógicos comunes de la investigación. También define conceptos como variables y fuentes de recolección de datos.
Unified communications (UC) integrates various communication systems and devices to allow users to easily collaborate in real-time. It streamlines communications, cuts costs by leveraging existing infrastructure, and ties together people, devices, and information. Key components of UC include unified messaging, instant messaging, presence/identity, integration with existing infrastructure like PBX systems, video conferencing, and linking UC with business processes. Companies realize value from UC by replacing insecure apps, automating workflows, reducing travel costs through virtual meetings, and providing a single identity for presence and communication across channels. When planning a UC strategy, companies should target objectives, take a phased approach, and partner with experts.
The document discusses the influence of ancient Greek philosophy on Western thought, focusing on several key philosophers and schools of thought. It introduces Socrates, Plato, and Aristotle, and examines Epicureanism and Stoicism in more depth. Stoicism in particular had a major influence on early Christianity and the writings of Paul. While Paul engaged with Stoic ideas and techniques, he focused his message on preaching Christ and his crucifixion.
El documento describe la historia del uso de la tierra como un camino de retorno para los sistemas de telégrafo en el siglo XIX. Inicialmente, este enfoque funcionó pero luego causó problemas de interferencia con el desarrollo de la telefonía. El documento también resume brevemente qué es un polo a tierra, incluyendo su función de desviar sobrecargas eléctricas a tierra para proteger a las personas y aparatos, y los tipos y materiales comunes utilizados en los polos a tierra.
Econ315 Money and Banking: Learning Unit 16: Law of One Price and Derivative ...sakanor
This document provides an overview of the law of one price and derivative markets. It defines arbitrage and how arbitrage works to eliminate price discrepancies between markets through changes in demand and supply. It also explains the four main types of financial derivatives - forwards, futures, options, and swaps. Forwards and futures are described as contracts that promise delivery of an underlying asset at a future date. Hedging using derivatives to offset long or short positions is also discussed.
A MULTI-STREAM HMM APPROACH TO OFFLINE HANDWRITTEN ARABIC WORD RECOGNITIONkevig
In This paper we presented new approach for cursive Arabic text recognition system. The objective is to propose methodology analytical offline recognition of handwritten Arabic for rapid implementation. The first part in the writing recognition system is the preprocessing phase is the preprocessing phase to prepare the data was introduces and extracts a set of simple statistical features by two methods : from a window which is sliding long that text line the right to left and the approach VH2D (consists in projecting every character on the abscissa, on the ordinate and the diagonals 45° and 135°) . It then injects the resulting feature vectors to Hidden Markov Model (HMM) and combined the two HMM by multi-stream approach.
A Novel Approach for Recognizing Text in Arabic Ancient Manuscripts ijnlc
In this paper a system for recognizing Arabic ancient manuscripts is presented. The system has been
divided into four parts. The first part is the image pre-processing where the text in the Arabic ancient
manuscript will be recognized as a collection of Arabic characters through three phases of processing. The
second part is the Arabic text analysis which consists of lexical analyzer; syntax analyzer; and semantic
analyzer. The output of this subsystem is an XML file format that represents the ancient manuscript text.
The third part is the intermediate text generation, in this part an intermediate presentation of the Arabic
text is generated from the XML text file. The fourth part of the system is the Arabic text generation, which
converts the generated text to a modern standard Arabic (MSA) language (this part has four phases: text
organizer; pre-optimizer; semantics generator; and post-optimizer).
A NOVEL APPROACH FOR RECOGNIZING TEXT IN ARABIC ANCIENT MANUSCRIPTSkevig
This document summarizes a research paper that presents a novel approach for recognizing text in Arabic ancient manuscripts. The proposed system has four main parts: 1) image pre-processing to recognize text as Arabic characters, 2) Arabic text analysis using lexical, syntax and semantic analysis, 3) generating intermediate text from the XML output, and 4) generating modern standard Arabic text. The key steps involve manuscript binarization, segmentation, feature extraction, and text recognition and generation. The goal is to convert ancient manuscript images into electronic text and translate the text into a more modern and understandable form.
A Comprehensive Study On Handwritten Character Recognition Systemiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document provides a comprehensive review of existing works in offline handwritten character recognition. It discusses the three major stages of any character recognition system: preprocessing, feature extraction, and classification. For preprocessing, it describes techniques like binarization, filtering, and morphological operations that are used to improve image quality. For feature extraction, it discusses various methods used to represent characters, including global transformations, statistical representations, and geometrical/topological features. Wavelet transforms are highlighted as a commonly used feature extraction technique. Finally, it provides an overview of literature on methods used in each stage of offline handwritten character recognition systems.
A Novel Approach for Bilingual (English - Oriya) Script Identification and Re...CSCJournals
In most of our official papers, school text books, it is observed that English words interspersed within the Indian languages. So there is need for an Optical Character Recognition (OCR) system which can recognize these bilingual documents and store it for future use. In this paper we present an OCR system developed for the recognition of Indian language i.e. Oriya and Roman scripts for printed documents. For such purpose, it is necessary to separate different scripts before feeding them to their individual OCR system. Firstly, we need to correct the skew followed by segmentation. Here we propose the script differentiation line-wise. We emphasize on Upper and lower matras associated with Oriya and absent in English. We have used horizontal histogram for line distinction belonging to different script. After separation different scripts are sent to their individual recognition engines.
Two Methods for Recognition of Hand Written Farsi CharactersCSCJournals
This document describes two methods for recognizing handwritten Farsi characters using neural networks and machine learning techniques. The first method uses wavelet transforms to extract features from character borders and trains a neural network classifier on these features. It achieves 86.3% accuracy on test data. The second method divides characters into groups based on visual properties, extracts moment features for each group, and uses Bayesian classification with a decision tree post-processing step. It achieves an overall recognition rate of 90.64% according to the results presented. Experimental evaluations of both methods on different datasets of handwritten Farsi characters are discussed.
RECOGNITION OF HANDWRITTEN MEITEI MAYEK SCRIPT BASED ON TEXTURE FEATURE kevig
Recognition of Manipuri Script called Meitei Mayek is still in the infant stage due to its complex structure. In this paper, an attempt has been made to develop an offline Meitei Mayek handwritten character recognition model by exploiting the texture feature, Local Binary Pattern (LBP). The system has been developed and evaluated on a large dataset consisting of 3,780 characters which are collected from different people of varying age group. The highest recognition rate achieved by the proposed method is 93.33% using Support Vector Machine (SVM). So, the contribution of this paper is bi-fold: firstly, a collection of a large handwritten corpus of Meitei Mayek Script and secondly developing character recognition model on the collected dataset.
IRJET- Real-Time Text Reader for English LanguageIRJET Journal
This document summarizes a research paper that presents a real-time text reader system for the English language. The system uses optical character recognition and support vector machines for text recognition and classification. It recognizes text from images, videos, and handwritten documents and classifies the text into predefined parts of speech categories for English. The system first detects text from the input source using OCR, then classifies and categorizes the recognized text.
DEVNAGARI DOCUMENT SEGMENTATION USING HISTOGRAM APPROACHijcseit
This document summarizes a research paper on Devnagari document segmentation using a histogram approach. It discusses challenges in segmenting the Devnagari script used for several Indian languages. A simple algorithm is proposed using horizontal and vertical histograms to segment documents into lines, words and characters. The algorithm achieves near 100% accuracy for line segmentation but lower accuracy for word and character segmentation due to complexities in the Devnagari script. Future work is needed to improve character segmentation handling connected and modified characters.
RECOGNITION OF CHEISING IYEK/EEYEK-MANIPURI DIGITS USING SUPPORT VECTOR MACHINESIJCSIT Journal
This paper describes the recognition of Cheising Iyek-Manipuri digits; handwritten as well as printed and comparison of recognition accuracy using Support Vector Machines (SVM). The paper also presents the steps starting right from binarization of scanned images in pre-processing till recognition of the digits using a trained model.
Correcting optical character recognition result via a novel approachIJICTJOURNAL
Optical character recognition (OCR) is a recognition system used to recognize the substance of a checked picture. This system gives erroneous results, which necessitates a post-treatment, for the sentence correction. In this paper, we proposed a new method for syntactic and semantic correction of sentences it is based on the frequency of two correct words in the sentence and a recursive technique. This approach starts with the frequency calculation of each two words successive in the corpora, the words that have the greatest frequency build a correction center. We found 98% using our approach when we used the noisy channel. Further, we obtained 96% using the same corpus in the same conditions.
Recognition Of Handwritten Meitei Mayek Script Based On Texture Featureijnlc
This document presents a method for recognizing handwritten characters of the Meitei Mayek script using texture features and a support vector machine classifier. A dataset of 3,780 handwritten characters collected from 70 people was used to develop and evaluate the recognition model. Local binary patterns were extracted as texture features from the pre-processed images. Using this approach, the highest recognition rate achieved on the dataset was 93.33%. This represents an improvement over previous work on recognizing handwritten Meitei Mayek characters. Future work could focus on developing models to recognize complete sentences instead of isolated characters.
Translation of sign language using generic fourier descriptor and nearest nei...ijcisjournal
Sign languages are used all over the world as a primary means of communication by deaf people. Sign
language translation is a promising application for vision-based gesture recognition methods. Therefore, it
is need such a tool that can translate sign language directly. This paper aims to create a system that can
translate static sign language into textual form automatically based on computer vision. The method
contains three phases, i.e. segmentation, feature extraction, and recognition. We used Generic Fourier
Descriptor (GFD) as feature extraction method and K-Nearest Neighbour (KNN) as classification
approach to recognize the signs. The system was applied to recognize each 120 stored images in database
and 120 images which is captured real time by webcam. We also translated 5 words in video sequences.
The experiment revealed that the system can recognized the signs with about 86 % accuracy for stored
images in database and 69 % for testing data which is captured real time by webcam.
This document summarizes a research paper that reviews different methods for scene text detection and the challenges associated with it. The paper begins with an introduction that describes the overall process of automated scene text detection systems. It then provides a literature review of various text detection methods proposed in previous research, which can be categorized as connected component based methods or texture based methods. Some example methods are described. The paper discusses challenges in scene text detection, such as variable imaging conditions, complex backgrounds, and a wide range of text sizes and fonts. Finally, it discusses performance metrics like precision, recall, and f-measure that are used to evaluate scene text detection methods based on a standard dataset.
El documento resume una inspección realizada a una construcción de cerramientos en el Urbanismo Hugo Chavez Frias en el estado Lara de Venezuela. Se detalla que los trabajos no han podido concluirse debido a la falta de materiales. Una posible solución sería permitir que la empresa contratista suministre los materiales faltantes para continuar y completar la construcción, la cual beneficiará a 260 familias con apartamentos. El documento incluye fotos que muestran el estado de la construcción y planos eléctricos y de plomería.
El documento presenta el marco metodológico de una investigación sobre metodología de la investigación en ingeniería de sistemas. Describe los elementos clave del marco metodológico como la modalidad y tipo de investigación, población y muestra, técnicas de recolección y análisis de datos, y las fases y pasos lógicos comunes de la investigación. También define conceptos como variables y fuentes de recolección de datos.
Unified communications (UC) integrates various communication systems and devices to allow users to easily collaborate in real-time. It streamlines communications, cuts costs by leveraging existing infrastructure, and ties together people, devices, and information. Key components of UC include unified messaging, instant messaging, presence/identity, integration with existing infrastructure like PBX systems, video conferencing, and linking UC with business processes. Companies realize value from UC by replacing insecure apps, automating workflows, reducing travel costs through virtual meetings, and providing a single identity for presence and communication across channels. When planning a UC strategy, companies should target objectives, take a phased approach, and partner with experts.
The document discusses the influence of ancient Greek philosophy on Western thought, focusing on several key philosophers and schools of thought. It introduces Socrates, Plato, and Aristotle, and examines Epicureanism and Stoicism in more depth. Stoicism in particular had a major influence on early Christianity and the writings of Paul. While Paul engaged with Stoic ideas and techniques, he focused his message on preaching Christ and his crucifixion.
El documento describe la historia del uso de la tierra como un camino de retorno para los sistemas de telégrafo en el siglo XIX. Inicialmente, este enfoque funcionó pero luego causó problemas de interferencia con el desarrollo de la telefonía. El documento también resume brevemente qué es un polo a tierra, incluyendo su función de desviar sobrecargas eléctricas a tierra para proteger a las personas y aparatos, y los tipos y materiales comunes utilizados en los polos a tierra.
Econ315 Money and Banking: Learning Unit 16: Law of One Price and Derivative ...sakanor
This document provides an overview of the law of one price and derivative markets. It defines arbitrage and how arbitrage works to eliminate price discrepancies between markets through changes in demand and supply. It also explains the four main types of financial derivatives - forwards, futures, options, and swaps. Forwards and futures are described as contracts that promise delivery of an underlying asset at a future date. Hedging using derivatives to offset long or short positions is also discussed.
A MULTI-STREAM HMM APPROACH TO OFFLINE HANDWRITTEN ARABIC WORD RECOGNITIONkevig
In This paper we presented new approach for cursive Arabic text recognition system. The objective is to propose methodology analytical offline recognition of handwritten Arabic for rapid implementation. The first part in the writing recognition system is the preprocessing phase is the preprocessing phase to prepare the data was introduces and extracts a set of simple statistical features by two methods : from a window which is sliding long that text line the right to left and the approach VH2D (consists in projecting every character on the abscissa, on the ordinate and the diagonals 45° and 135°) . It then injects the resulting feature vectors to Hidden Markov Model (HMM) and combined the two HMM by multi-stream approach.
A Novel Approach for Recognizing Text in Arabic Ancient Manuscripts ijnlc
In this paper a system for recognizing Arabic ancient manuscripts is presented. The system has been
divided into four parts. The first part is the image pre-processing where the text in the Arabic ancient
manuscript will be recognized as a collection of Arabic characters through three phases of processing. The
second part is the Arabic text analysis which consists of lexical analyzer; syntax analyzer; and semantic
analyzer. The output of this subsystem is an XML file format that represents the ancient manuscript text.
The third part is the intermediate text generation, in this part an intermediate presentation of the Arabic
text is generated from the XML text file. The fourth part of the system is the Arabic text generation, which
converts the generated text to a modern standard Arabic (MSA) language (this part has four phases: text
organizer; pre-optimizer; semantics generator; and post-optimizer).
A NOVEL APPROACH FOR RECOGNIZING TEXT IN ARABIC ANCIENT MANUSCRIPTSkevig
This document summarizes a research paper that presents a novel approach for recognizing text in Arabic ancient manuscripts. The proposed system has four main parts: 1) image pre-processing to recognize text as Arabic characters, 2) Arabic text analysis using lexical, syntax and semantic analysis, 3) generating intermediate text from the XML output, and 4) generating modern standard Arabic text. The key steps involve manuscript binarization, segmentation, feature extraction, and text recognition and generation. The goal is to convert ancient manuscript images into electronic text and translate the text into a more modern and understandable form.
A Comprehensive Study On Handwritten Character Recognition Systemiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document provides a comprehensive review of existing works in offline handwritten character recognition. It discusses the three major stages of any character recognition system: preprocessing, feature extraction, and classification. For preprocessing, it describes techniques like binarization, filtering, and morphological operations that are used to improve image quality. For feature extraction, it discusses various methods used to represent characters, including global transformations, statistical representations, and geometrical/topological features. Wavelet transforms are highlighted as a commonly used feature extraction technique. Finally, it provides an overview of literature on methods used in each stage of offline handwritten character recognition systems.
A Novel Approach for Bilingual (English - Oriya) Script Identification and Re...CSCJournals
In most of our official papers, school text books, it is observed that English words interspersed within the Indian languages. So there is need for an Optical Character Recognition (OCR) system which can recognize these bilingual documents and store it for future use. In this paper we present an OCR system developed for the recognition of Indian language i.e. Oriya and Roman scripts for printed documents. For such purpose, it is necessary to separate different scripts before feeding them to their individual OCR system. Firstly, we need to correct the skew followed by segmentation. Here we propose the script differentiation line-wise. We emphasize on Upper and lower matras associated with Oriya and absent in English. We have used horizontal histogram for line distinction belonging to different script. After separation different scripts are sent to their individual recognition engines.
Two Methods for Recognition of Hand Written Farsi CharactersCSCJournals
This document describes two methods for recognizing handwritten Farsi characters using neural networks and machine learning techniques. The first method uses wavelet transforms to extract features from character borders and trains a neural network classifier on these features. It achieves 86.3% accuracy on test data. The second method divides characters into groups based on visual properties, extracts moment features for each group, and uses Bayesian classification with a decision tree post-processing step. It achieves an overall recognition rate of 90.64% according to the results presented. Experimental evaluations of both methods on different datasets of handwritten Farsi characters are discussed.
RECOGNITION OF HANDWRITTEN MEITEI MAYEK SCRIPT BASED ON TEXTURE FEATURE kevig
Recognition of Manipuri Script called Meitei Mayek is still in the infant stage due to its complex structure. In this paper, an attempt has been made to develop an offline Meitei Mayek handwritten character recognition model by exploiting the texture feature, Local Binary Pattern (LBP). The system has been developed and evaluated on a large dataset consisting of 3,780 characters which are collected from different people of varying age group. The highest recognition rate achieved by the proposed method is 93.33% using Support Vector Machine (SVM). So, the contribution of this paper is bi-fold: firstly, a collection of a large handwritten corpus of Meitei Mayek Script and secondly developing character recognition model on the collected dataset.
IRJET- Real-Time Text Reader for English LanguageIRJET Journal
This document summarizes a research paper that presents a real-time text reader system for the English language. The system uses optical character recognition and support vector machines for text recognition and classification. It recognizes text from images, videos, and handwritten documents and classifies the text into predefined parts of speech categories for English. The system first detects text from the input source using OCR, then classifies and categorizes the recognized text.
DEVNAGARI DOCUMENT SEGMENTATION USING HISTOGRAM APPROACHijcseit
This document summarizes a research paper on Devnagari document segmentation using a histogram approach. It discusses challenges in segmenting the Devnagari script used for several Indian languages. A simple algorithm is proposed using horizontal and vertical histograms to segment documents into lines, words and characters. The algorithm achieves near 100% accuracy for line segmentation but lower accuracy for word and character segmentation due to complexities in the Devnagari script. Future work is needed to improve character segmentation handling connected and modified characters.
RECOGNITION OF CHEISING IYEK/EEYEK-MANIPURI DIGITS USING SUPPORT VECTOR MACHINESIJCSIT Journal
This paper describes the recognition of Cheising Iyek-Manipuri digits; handwritten as well as printed and comparison of recognition accuracy using Support Vector Machines (SVM). The paper also presents the steps starting right from binarization of scanned images in pre-processing till recognition of the digits using a trained model.
Correcting optical character recognition result via a novel approachIJICTJOURNAL
Optical character recognition (OCR) is a recognition system used to recognize the substance of a checked picture. This system gives erroneous results, which necessitates a post-treatment, for the sentence correction. In this paper, we proposed a new method for syntactic and semantic correction of sentences it is based on the frequency of two correct words in the sentence and a recursive technique. This approach starts with the frequency calculation of each two words successive in the corpora, the words that have the greatest frequency build a correction center. We found 98% using our approach when we used the noisy channel. Further, we obtained 96% using the same corpus in the same conditions.
Recognition Of Handwritten Meitei Mayek Script Based On Texture Featureijnlc
This document presents a method for recognizing handwritten characters of the Meitei Mayek script using texture features and a support vector machine classifier. A dataset of 3,780 handwritten characters collected from 70 people was used to develop and evaluate the recognition model. Local binary patterns were extracted as texture features from the pre-processed images. Using this approach, the highest recognition rate achieved on the dataset was 93.33%. This represents an improvement over previous work on recognizing handwritten Meitei Mayek characters. Future work could focus on developing models to recognize complete sentences instead of isolated characters.
Translation of sign language using generic fourier descriptor and nearest nei...ijcisjournal
Sign languages are used all over the world as a primary means of communication by deaf people. Sign
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Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
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Chapter 2
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This Dissertation explores the particular circumstances of Mirzapur, a region located in the
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environment for investigating the changes in vegetation cover dynamics. Our study utilizes
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providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
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9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Recognition of Tifinaghe Characters Using a Multilayer Neural Network
1. Rachid EL Ayachi, Mohamed Fakir & Belaid Bouikhalene
International Journal Of Image Processing (IJIP), Volume (5) : Issue (2) : 2011 109
Recognition of Tifinaghe Characters Using a Multilayer Neural
Network
Rachid EL Ayachi rachidieea@yahoo.fr
Faculty of Sciences and Techniques/
Computer Sciences Department
Sultan Moulay Slimane University
Béni-Mellal, BP:523, Morocco
Mohamed Fakir medfaki@yahoo.fr
Faculty of Sciences and Techniques/
Computer Sciences Department
Sultan Moulay Slimane University
Béni-Mellal, BP:523, Morocco
Belaid Bouikhalene bbouikhalene@yahoo.fr
Faculty of Sciences and Techniques/ Information
processing and telecommunications teams
Sultan Moulay Slimane University
Béni-Mellal, BP:523, Morocco
Abstract
In this paper, we present an off line Tifinaghe characters recognition system. Texts are scanned
using a flatbed scanner. Digitized text are normalised, noise is reduced using a median filter,
baseline skew is corrected by the use of the Hough transform, and text is segmented into line and
lines into words. Features are extracted using the Walsh Transformation. Finally characters are
recognized by a multilayer neural network.
Keywords: Tifinaghe Characters, Baseline Skew Correction, Segmentation, Walsh Transform,
Hough Transform, Neural Network, Recognition.
1. INTRODUCTION
Optical Character Recognition (OCR) is one of the most successful applications of automatic
pattern recognition. It is a very active field of research and development.
Several studies have been conducted on Latin, Arabic and Chinese characters [1, 2, 3, 4, 5, 6, 7,
8, 9 ]. However, for Tifinaghe characters system few works was done [13, 14, 15, 16].
Succession of operations in most digital image recognition system can be divided into three
stages. First stage is a pre-processing including thresholding improving image quality,
segmentation and son on. Second, features extraction for avoiding data abundance and reducing
its dimension. Third stage is a classification. During this stage classes name is joint with unknown
image by extracted features analyses and matching its representatives of the class, which the
classifier has trained at a stage of training.
In this study a recognition system (Figure 1) for the recognition of Tifinaghe characters issued
from an image scanner is presented. Initially, an image that contains Tifinaghe characters is
normalized and segmented to produce a data base. Then, we applied the approach of Walsh
Transform to extracted features which are used in the classification phase with a multilayer neural
network.
The organisation of this paper is as follows. In section 2 characteristics of Tifinagh characters are
given. In section 3 pre-processing process is described. Features extraction step is described in
2. Rachid EL Ayachi, Mohamed Fakir & Belaid Bouikhalene
International Journal Of Image Processing (IJIP), Volume (5) : Issue (2) : 2011 110
section 4. Section 5 deals with the recognition step. Experimental results are given in section 6.
Finally, this work is ended by a conclusion.
2. TIFINAGHE CHARACTERS
The Tifinaghe script is used by approximately 20 million people who speak varieties of languages
commonly called Berber or Amazigh. The three main varieties in Morocco are known as Tarifite,
Tamazighe, and Tachelhite. In Morocco, more than 50% of the population speaks Berber. In
accordance with recent governmental decisions, the teaching of the Berber language, written in
the Tifinaghe script, will be generalized and compulsory in Tifinaghe is an alphabetic writing
system. It uses spaces to separate words and makes use of Western punctuation. The earliest
variety of the Berber alphabet is Libyan. Two forms exist: a Western form and an Eastern form.
The Western variety was used along the Mediterranean coast from Kabylia to Morocco and most
probably to the Canary Islands. The Eastern variety, old Tifinaghe, is also called Libyan-Berber or
old Tuareg. It contains signs not found in the Libyan variety and was used to transcribe Old
Tuareg. A number of variants of Neo-Tifinaghe exist, the first of which was proposed in the 1960s
by the Académie Berbère. That variant has spread in Morocco and in Algeria, especially in
Kabylia. Other Neo-Tifinaghe systems are nearly identical to the Académie Berbère system. The
encoding in the Tifinaghe block is based on the Neo-Tifinaghe systems. Historically, Berber texts
did not have a fixed direction. Early inscriptions were written horizontally from left to right, from
right to left, vertically (bottom to top, top to bottom); boustrophedon directionality was also known.
FIGURE 1: Tifinaghe recognized system.
Modern-day Berber script is most frequently written in horizontal lines from left to right; therefore
the bidirectional class for Tifinaghe letters is specified as strong left to right. The encoding
consists of four Tifinaghe character subsets: the basic set of the ″ Institut Royal de la Culture
Amazighe (IRCAM) ″, the extended IRCAM set, other Neo-Tifinaghe letters in use, and modern
Tuareg letters. The first subset represents the set of characters chosen by IRCAM to unify the
orthography of the different Moroccan modern day Berber dialects while usingthe historical
Tifinaghe script. The alphabet Tifinaghe adopted by IRCAM [9] is composed of thirty-three
characters representing consonants and vowels as shown in Table1.
3. Rachid EL Ayachi, Mohamed Fakir & Belaid Bouikhalene
International Journal Of Image Processing (IJIP), Volume (5) : Issue (2) : 2011 111
TABLE 1: Tifinaghe characters adopted by IRCAM.
3. PREPROCESSING
Pre-processing is the first part of Tifinaghe characters recognition system which covers four
functions to produce a cleaned up version of the original image so that it can be used directly and
efficiently by the feature extraction components of the OCR. These functions are: scanning the
text and digitizing it into a digital image and cleaning it (by medium filter for example), converting
the grey-scale image into binary image, normalizing the text, detecting and correcting Baseline
Skew, and segmenting the text into lines and the lines into characters.
3.1 Normalization of the position
The position normalization is designed to eliminate unwanted areas and reduce the processing
time. In this operation, firstly, we compute the horizontal and vertical histograms, secondly, we
scan the horizontal histogram in two directions: from top to bottom and bottom to top respectively
until the first meeting of black pixels, finally, we scan the vertical histogram in two directions: from
left to right and right to left respectively until the first meeting of black pixels. After obtaining the
positions of first black pixels, unwanted areas are eliminated in the image as shown in (Fig. 2).
(a) (b)
(d) (c)
FIGURE 2: (a) Before normalization, (d) After normalization,
(b) Horizontal histogram and (c) Vertical histogram
3.2 Baseline Skew Detection and Correction
A skew angle is the angle that the text lines of the document image make with the horizontal
direction. The skew correction is necessary for the success of many OCR systems.
4. Rachid EL Ayachi, Mohamed Fakir & Belaid Bouikhalene
International Journal Of Image Processing (IJIP), Volume (5) : Issue (2) : 2011 112
There are many methods to use for detecting skew angle, such as: the Trincklin method that uses
the process of least squares to estimate skew angle, the Projection method is based on the
calculation of horizontal histogram of the image, Hough transform, Fourier transform, Correlation
lines, k-nearest neighbors. [17,18,19].
In this paper, the operation of skew correction is to estimate a skew angle θs using the Hough
transform and to rotate the image by θs in the opposite direction, which gave the good results as
showed in (Fig.3).
(a) (b)
(c) (d)
FIGURE 3: (a) Before correction, (c) After correction,
(b) Horizontal histogram before correction,
(d) Horizontal histogram after correction.
3.3 Segmentation
The last function to apply into pre-processing part is the segmentation; it is used to detect lines
and characters in the image.
This method covers two steps: firstly, we use the horizontal histogram to detect lines; secondly,
we use the vertical histogram to detect characters.
In the horizontal histogram, we browse from top to bottom until the first line containing at least
one black pixel, the line is the beginning of the first line of text, then we continue traverse until a
line that contains only white pixels, this line corresponds to the end of the first line of text. With
the same way, we continue to detect other text lines.
In the vertical histogram, for each line of text, we browses from left to right until the first column
containing at least one black pixel, this column is the beginning of the first character, then we
continue traverse until a column that contains only white pixels, this column corresponds to the
end of the first character. We continue detecting other characters of text with the same way.
(a) (b)
FIGURE 4: (a) lines segmentation, (b) Characters segmentation
5. Rachid EL Ayachi, Mohamed Fakir & Belaid Bouikhalene
International Journal Of Image Processing (IJIP), Volume (5) : Issue (2) : 2011 113
4. FEATURES EXTRACTION
The second phase of Tifinaghe characters recognition system is Features extraction. Several
methods can be used to compute the features: invariant momentsm Walsh transformation [20,21]
etc,.
In this recognition system, we use Walsh Transformation to extract features, because this method
is independent to translation, rotation and scale change.
The Walsh transformation is given by:
∑∑
−
=
−
=
=
1
0
1
0
),,,(),(),(
N
x
N
y
vuyxgyxfvuW (2)
Where f(x, y), is the intensity of the pixel with the coordinates (x, y) in the original binary image.
The size of image f is N*N, and 1-N,…0,=, vu , thus we compute N2 Walsh transforms,
g(x, y, u, v) is the Kernel function given by the following form:
∏
−
=
+ −−−−
−=
1
0
)()()()( 11
)1()/1(),,,(
n
i
vbybubxb iniini
Nvuyxg (3)
Where )(xbi is the ith bit in the binary expansion of x (it is equal either 0 or 1).
Table2 represents the seven first elements of the vector Walsh calculated for one character with
his four transformations:
0 0 0 -0.0029
-0.0029 -0.0029 -0.0029 -0.0059
-0.0064 -0.0064 -0.0059 -0.0088
-0.0098 -0.0098 -0.0093 -0.0118
-0.0132 -0.0132 -0.0127 -0.0137
-0.0167 -0.0167 -0.0162 -0.0152
-0.0201 -0.0201 -0.0196 -0.0172
TABLE 2: Walsh Coefficients.
5. CHARACTER RECOGNITION
In the character recognition system, the recognition is the last phase which is used to identify the
segmented character. Where we use the Neural Network approach for several reasons: the
execution time is reduced and the principle of Neural Network is simple and effective.
In this phase a neural network is used [12], from an analogy with the biological neuron, is a
processor that implements simple inputs and can connect with others to form a network that can
achieve a relationship any entry-exit.
6. Rachid EL Ayachi, Mohamed Fakir & Belaid Bouikhalene
International Journal Of Image Processing (IJIP), Volume (5) : Issue (2) : 2011 114
The Neural Network as shown in (Fig.5) represents an example of Neural Network multilayer
which contains one hidden layer. It has:
FIGURE 5: Neural Network
- An input layer of 49 (Walsh vector) inputs cells ii XE = (the cells represents the inputs iE of
Network).
- A hidden layer of 3 activations Neural jY .
- An output layer of 6 activations Neural kZ .
- 49×3 connections between input layer and hidden layer, each weighted by jiV .
- 3×6 connections between hidden layer and output layer, each weighted by kjW .
- 0X , 0Y are initialled values (scalars).
The operation of Neural Network as shown in (Fig.5) contains five steps:
- Step 1: (Initializing weights of connexions), the weights are randomly selected.
- Step 2: (propagation of inputs)
The inputs iE are presented to input layer: ii EX = .
We propagate to hidden layer:
+= ∑=
49
1
0
i
jiij XVXfY (4)
After for hidden layer to output layer:
7. Rachid EL Ayachi, Mohamed Fakir & Belaid Bouikhalene
International Journal Of Image Processing (IJIP), Volume (5) : Issue (2) : 2011 115
+= ∑=
3
1
0
j
kjjk YWYfZ (5)
The values 0X and 0Y are means (scalars).
f is the activation function which is given by
))exp(1/(1)( xxf −+= (6)
- Step 3: (Error back propagation)
For each example of applied learning base input of the network, we calculate the error at output
layers, i.e. the difference between the desired output kS and kZ actual output:
( )( )kkkkk ZSZZE −−= 1 (7)
We propagate this error on the hidden layer; the error of each neuron of the hidden layer is given
by:
( )∑=
−=
6
1
.1
k
kkjjjj EWYYF (8)
- Step 4: (Correction of connections weights )
We change the weights of connections:
- Between input layer and hidden layer:
jiji FXV ..η=∆ And jFY .0 η=∆ (9)
- Between hidden layer and output layer:
kjkj EYW ..η=∆ And kEX .0 η=∆ (10)
Where η is the learning rate comprised between 0 and 1. This is experimentally determined
)9.0( =η
- Step 5: (Loop)
Loop in step tow to a criterion to define.
(Error threshold = 0.0001, Number of iterations = 50000)
After the learning of Network and the execution of Tifinaghe Characters Recognition System to
recognize a Text, we use the Euclidian distance to identify the characters of Text.
2/16
1
2
)(),(
−= ∑=i
ikik ototd (11)
Where, kt is a desired output and o is the output of Network.
6. EXPERIMENTALS RESULTS
A Data Base used in this system contains 360 images which represents the Tifinaghe characters.
All tests are applied on 158 characters.
Tests applied on several images gave the good results, which demonstrate the performance of
the recognition system. Table 3 illustrated some recognized words.
8. Rachid EL Ayachi, Mohamed Fakir & Belaid Bouikhalene
International Journal Of Image Processing (IJIP), Volume (5) : Issue (2) : 2011 116
Text to be recognized recognition Results
TABLE 3: Examples of some words used for the test of the recognition method.
Number of
hidden layer
Recognition
rates
Error
rates
Computing
time
1 93.52% 6.48% 21.23s
2 86.71% 13.29% 28.65s
3 87.34% 12.66% 36.21s
TABLE 4: Recognition rates, Error rates and Computing times
A close inspection of Table 4 show that the recognition rate using one hidden layer is higher than
those obtained by tow hidden layers, but error rates and computing time using two hidden layers
are less than to those obtained by one hidden layer. Table5 illustrates the misrecognised
characters. These error are caused by noise or rotation. The method has been implemented in
Matlap software on a core (TM) Duo CPU T5870 @ 2.00 GHz
Noise Rotation
TABLE 5: Misrecognised characters
7. CONCLUSION
The subject of the work developed in this paper is to achieve system recognition of Tifinaghe
characters. This system consists of three phases applied on the input image: pre-processing
features extraction and recognition. Pre-processing phase includes normalisation baseline skew
correction and segmentation. The features extraction phase is used to compute the characters
features using Walsh Transformation for the reasons of invariance to translation, rotation and
scale change. In the recognition phase a multilayer neural network is used to classify characters.
Experimental results showed that this method give good recognition rate in a final conclusion,
neural network seems to be better than other techniques used for recognition
8. REFERENCES
[1] R. M. Bozinovic and S. N. Shihari, Off Line Cursive Script Word Recognition, IEEE
Trans.Pattern Anal. Mach. Intell. PAMI 11, 1989, pp. 68- 83.
[2] M. K. Brown, pre-processing techniques for cursive word recognition, Pattern Recognition,
Vol.13, N°.5, pp: 447-451, 1983.
10. Rachid EL Ayachi, Mohamed Fakir & Belaid Bouikhalene
International Journal Of Image Processing (IJIP), Volume (5) : Issue (2) : 2011 118
[18] A. Sehad, L. Mezai, M.T. Laskri, M. Cheriet, Détection de l’inclinaison des documents arabes
imprimés.
[19] Attila Fazekas and Andras Hajdu Recognizing Type set Documents using Walsh , JCIT-
CIT 9, 2-2001, 101-112.
[20] Ibrahim S. I. Abuhaiba, Arabic Font Recognition Using Decision Trees Built From Common
Words, JCIT-CIT 13, 3-2005, 211-223.