Character Recognition Method Research Articles

The Implementation Of Sound Output Image Processing With Wireless Based On Control To Vehicle Identification

Vehicle license plate identification is a system that utilizes image processing techniques to identify vehicles automatically from the vehicle license plate image. This system can be applied in various applications such as tracking stolen cars in homes or parking lots, automatic parking ticket payments, and collecting traffic statistics. Parking space as a facility to park motorists’ vehicles to be more organized. Parking space is currently still done conventionally. The development of technology, conventional control systems developed into electronic access systems, one of which is a monitoring system. The monitoring system using CCTV is a monitoring device, but it is expensive at the cost of electrical, network installation and limited mobility of device placement. In this study a Raspberry Pi-based room control system was designed. Raspberry Pi to build inexpensive monitoring systems and electricity-saving technology. This study aims to create a remote-based IP camera control system wirelessly for processing image plates of vehicle sound output with Raspberry Pi. The system starts by capturing the vehicle license plate with a Raspberry Pi-based camera or webcam. The control system is created using a motion program, and plate image processing uses digital image processing techniques and the Optical Character Recognition (OCR) method. The results of the system research can identify a Raspberry Pi server-based vehicle plate. The result is the vehicle license plate or text and voice output with text to speech technology. The results of implementing image processing from nine plate tests are 90.278%.

Research on Artificial Intelligence Machine Learning Character Recognition Method Based on Feature Fusion

With the continuous development of artificial intelligence technology, people’s research on it has become more in-depth and machine learning has become a new research core issue in artificial intelligence. Before artificial intelligence technology was mature, it was a very difficult task to realize automatic character recognition and input. At present, human beings have made some progress in intelligent robots, voice recognition and network search. The text recognition method based on machine learning is of great significance to the promotion of information technology. Text recognition based on machine learning, although it has formed relatively rich theoretical support in principle and skill, but still faces various factors during actual practice, making its recognition effect difficult to be reflected. Based on the feature fusion perspective, this paper combines the background and main identification methods of text recognition in artificial intelligence machine learning to analyze the purpose, which lays a foundation for future specific applications.

Camshot-Hand Written Text Recognition and Object Detection Application

Text recognition is a technology which has ability to recognize text from images automatically developed in computer device. Tremendous demand is seen for storing the information available in physical papers into a storage disks and can be reuse this data by searching process. A simple way is to scan documents and store it as image. But reusing this data is becomes difficult to read contents and search these documents line by line or word by word. Quality of images and font characteristics of paper are main concerns. Computer devices are unable to identify these characters while reading them. Thus, character recognition methods are much needed to identify texts from images which converts paper format to digital format. In this paper we have discuss a method for text recognition from images using google firebase services like ML kit, in particular order of different processing module for better understanding.

Broken Character Recognition using Connected Components and Convolutional Neural Network

Recognizing broken characters in scanned and ancient scanned text document is not easy because the characters may be broken and unclear. Many researches have been carried to recognize these broken characters. In this research paper we have described a new broken characters recognition method for English text documents only. The proposed method uses a hybrid approach which uses connected component concepts and convolutional neural network to identify the broken characters. The input to the approach is scanned or ancient text document which contains unclear text that is difficult to recognize and hence our new proposed methodology will recognize these characters with greater accuracy and it will give the recognized characters to the user. The projected technique has attained a precision up to 92% in recognition.

A Tabulation method for Character Recognition using Haar wavelet in 2D images

Character perception is a unique way to diagnostic the unidentifiable character into understandable text. Nowadays, with the increasing urge of getting information digital media has been roped in because of its convenient use. Hence the printed media needs to get into digital format but the printed text needs to get recognized by the computer this difficulty is solved by the pattern matching technique and haar wavelet method to recognize the character ,decompose it and frame it into understandable format keeping its prototype in mind this method can turn boon for the unidentifiable text also it can overcome the problems of printed text character perception system help human ease and reduce their jobs of manually handling and processing of document. The proposed method showed that the recognition of text is reliable and significantly accelerates data processing. Evaluation is executed as a collection of degraded documents which are images, indicates the proposed work will be well suited to reduce the noise by improving the pixel quality in which it will be rendered. The result analysis performed by using image quality matrix which are Mean Squared Error, Signal to Noise ratio, Structural Content, Normalized Cross Correlation, Maximum Difference, Normalize Absolute Error, Average difference. All the parameter shows effective results. Keywords — Character Perception, Haar Wavelet, Image Quality Matrix, Pattern Matching, Recognition of Text.

Cursive Character Recognition in Natural Scene Images Using a Multilevel Convolutional Neural Network Fusion

The accuracy of current natural scene text recognition algorithms is limited by the poor performance of character recognition methods for these images. The complex backgrounds, variations in the writing, text size, orientations, low resolution and multi-language text make recognition of text in natural images a complex and challenging task. Conventional machine learning and deep learning-based methods have been developed that have achieved satisfactory results, but character recognition for cursive text such as Arabic and Urdu scripts in natural images is still an open research problem. The characters in the cursive text are connected and are difficult to segment for recognition. Variations in the shape of a character due to its different positions within a word make the recognition task more challenging than non-cursive text. Optical character recognition (OCR) techniques proposed for Arabic and Urdu scanned documents perform very poorly when applied to character recognition in natural images. In this paper, we propose a multi-scale feature aggregation (MSFA) and a multi-level feature fusion (MLFF) network architecture to recognize isolated Urdu characters in natural images. The network first aggregates multi-scale features of the convolutional layers by up-sampling and addition operations and then combines them with the high-level features. Finally, the outputs of the MSFA and MLFF networks are fused together to create more robust and powerful features. A comprehensive dataset of segmented Urdu characters is developed for the evaluation of the proposed network models. Synthetic text on the patches of images with real natural scene backgrounds is generated to increase the samples of infrequently used characters. The proposed model is evaluated on the Chars74K and ICDAR03 datasets. To validate the proposed model on the new Urdu character image dataset, we compare its performance with the histogram of oriented gradients (HoG) method. The experimental results show that the aggregation of multi-scale and multilevel features and their fusion is more effective, and outperforms other methods on the Urdu character image and Chars74K datasets.

OCR-Nets: Variants of Pre-trained CNN for Urdu Handwritten Character Recognition via Transfer Learning

Abstract Deep Convolutional neural networks (CNN) have been among the utmost competitive neural network architectures and have set the state-of-the-art in various fields of computer vision. In this paper, we present OCR-Nets, variants of (AlexNet & GoogleNet) for recognition of handwritten Urdu characters through transfer learning. Our proposed networks are experimented using an integrated dataset. To compare the recognition rate with traditional character recognition methods and to confirm the fairness of the experiment an additional Urdu character dataset is manually generated with different fonts and size. The experimental result shows that OCR-AlexNet and OCR-GoogleNet produce significant performance gains of 96.3% and 94.7% averaged success rate respectively.

Text Extraction and Recognition in Natural Scene Images using Contourlet Transform and PNN

Of late, the rapid development in the technology and multimedia capability in digital cameras and mobile devices has led to ever increasing number of images or multi-media data to the digital world. Particularly, in natural scene images, the text content provides explicit information to understand the semantics of images. Therefore, a system developed for extracting and recognizing texts accurately from natural scene images, in real-time, has significant relevance to numerous applications such as, assistive technology for people with vision impairment, tourist with language barrier, vehicle number plate detection, street signs, advertisement bill boards, robotics, etc. The extraction of the texts from natural scene images is a formidable task due to large variations in character fonts, styles, sizes, text orientations, presence of complex backgrounds and varying light conditions. The main focus of this research paper is to propose a novel hybrid approach for automatic detection, localization, extraction and recognition of text in natural scene images with cluttered background. Firstly, image regions with text are detected using edge features (GLCM) extracted from Contourlet transformed image and SVM (Support Vector Machine) classifier. Secondly, horizontal projection is applied on text regions for segmenting lines and vertical projection is applied on each text line for segmenting characters. The proposed method for text extraction has produced the precision, recall, F-Score and accuracy of 98.50%, 90.85.62%, 95.00%, and 89.90%, respectively. And, these results prove that the proposed method is efficient. Further, the so extracted characters are processed for recognition using contourlet transform and Probabilistic Neural Network (PNN) classifier. The computed features are moment invariants. Only the English script is considered for the experimentation. The proposed character recognition method has accuracy of 79.07%, which is higher in comparison to accuracy of 75.15% obtained by KNN (K-Nearest Neighbors) classifier

A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS.

Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.

A neural-network-based framework for cigarette laser code identification

The identification of cigarette laser codes is important in distinguishing the authenticity of tobacco. However, the existing character recognition methods have limited use in the identification due to the complex background in cigarette images. To address this issue, we propose a novel neural-network-based framework in this paper. Specifically, the framework includes three major steps. Firstly, a principal component analysis neural network is designed for the inclination correction progress to overcome the strong noise interferences. Then a novel algorithm is proposed to adaptively utilize the prior partition information for better character segmentation. Finally, a CNN model is designed to extract irregular features for character identification. By doing this, the proposed framework alleviates the influence of diverse backgrounds and keeps useful features at the same time. Additionally, we give an insight analysis on the character recognition based on the proposed method. The performance of the framework is evaluated on an image set composed of 100 cigarette laser code photos, whose results demonstrate that our framework can bring about 30% improvement in recognition accuracy compared to baseline methods. The good performance indicates a huge potential of our framework on practical applications.

Research on License Plate Image Segmentation and Intelligent Character Recognition

With the accumulation of people’s wealth and the improvement of purchasing power, more and more people are buying cars as a means of travel. Walking and cycling of the past have now become a car trip. License plate recognition technology is especially important in intelligent transportation systems. It has been widely used in large shopping malls or supermarket parking lots, highway toll stations, speeding violation supervision and other fields. However, the accuracy and efficiency of license plate image recognition are insufficient. To solve the above problems, we propose a license plate character recognition method based on local HOG and layered LBP feature fusion from the perspectives of image pre-processing, license location, characters’ segmentation and recognition. First, pre-processing the license image area by highlighting the license plate image; then, the license plate is positioned based on wavelet decomposition and brightness moment; next the tilted license plate image is corrected, the license plate frame is adjusted, and characterization is performed by using the improved projection method based on the fact that the projection of the character is a single peak or a double peak. Finally, the local HOG and hierarchical LBP feature fusion methods are used to identify the license characters. The results show that the license plate’s character recognition rate of the proposed method reaches 99.71%, and the time taken is small. This not only improves the character recognition rate, but also saves recognition time. The results show that the method has important practical significance in license plates’ recognition.

An Accuracy Examination of OCR Tools

In this research paper, the authors have aimed to do a comparative study of optical character recognition using different open source OCR tools. Optical character recognition (OCR) method has been used in extracting the text from images. OCR has various applications which include extracting text from any document or image or involves just for reading and processing the text available in digital form. The accuracy of OCR can be dependent on text segmentation and pre-processing algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, a complex background of image etc. From vehicle number plate the authors tried to extract vehicle number by using various OCR tools like Tesseract, GOCR, Ocrad and Tensor flow. The authors in this research paper have tried to diagnose the best possible method for optical character recognition and have provided with a comparative analysis of their accuracy

Text Recognition and Machine Learning: For Impaired Robots and Humans

As robots and machines become more reliable, developing tools that utilize their potential in manufacturing and beyond is an important step being addressed by many, including the LIMDA team at the University of Alberta. A common and effective means to improve artificial performance is through optical character recognition methods. Within the category of artificial intelligence under classification machine learning, research has focussed on the benefits of convolutional neural networks (CNN) and the improvement provided compared to its parent method, neural networks. Neural networks serious flaw comes from memorization and the lack of learning about what the images contain, while CNN's combat those issues. CNN’s are designed to connect information received by the network and begins to closely mimic how humans experience learns. Using the programming language Python and machine learning libraries such as Tensorflow and Keras, different versions of CNN’s were tested against datasets containing low-resolution images with handwritten characters. The first two CNN’s were trained against the MNIST database against digits 0 through 9. The results from these tests illustrated the benefits of elements like max-pooling and the addition of convolutional layers. Taking that knowledge a final CNN was written to prove the accuracy of the algorithm against alphabet characters. After training and testings were complete the network showed an average 99.34% accuracy and 2.23% to the loss function. Time-consuming training epochs that don’t wield higher or more impressive results could also be eliminated. These and similar CNN’s have proven to yield positive results and in future research could be implemented into the laboratory to improve safety. Continuing to develop this work will lead to better translators for language, solid text to digital text transformation, and aides for the visual and speech impaired.

Improvement of Image Binarization Methods Using Image Preprocessing with Local Entropy Filtering for Alphanumerical Character Recognition Purposes.

Automatic text recognition from the natural images acquired in uncontrolled lighting conditions is a challenging task due to the presence of shadows hindering the shape analysis and classification of individual characters. Since the optical character recognition methods require prior image binarization, the application of classical global thresholding methods in such case makes it impossible to preserve the visibility of all characters. Nevertheless, the use of adaptive binarization does not always lead to satisfactory results for heavily unevenly illuminated document images. In this paper, the image preprocessing methodology with the use of local image entropy filtering is proposed, allowing for the improvement of various commonly used image thresholding methods, which can be useful also for text recognition purposes. The proposed approach was verified using a dataset of 140 differently illuminated document images subjected to further text recognition. Experimental results, expressed as Levenshtein distances and F-Measure values for obtained text strings, are promising and confirm the usefulness of the proposed approach.

Multi-angle automotive fuse box detection and assembly method based on machine vision

With the detection and assembly of automobile fuse box entering the automation stage, the traditional method has low detection accuracy, poor assembly efficiency and cannot automatically adjust the torque of the torque wrench according to the production needs. Therefore, it is necessary to propose a new method to improve the system’s shortcomings and improve the system’s application. This study proposes a multi-angle automotive fuse box detection and assembly method based on machine vision. Firstly, the HSV color extraction method and OCR character recognition method are used to obtain the fuse color and model on the fuse box, and the fuses of each model in the fuse box are quickly detected and recognized. Then, the upper surface image of the fuse box is captured by two cameras with different viewing angles. The coordinates and depth of field information generated by the difference in viewing angle are used to judge the position of the torque wrench, and the torque of the torque wrench is automatically adjusted. Finally, the prototype of the detection and assembly system is designed. The simulation results show that the detection accuracy, detection speed and reliability of the method reach a high level. The field test results of a certain type of fuse box on the prototype show that the recognition speed is about 0.085 s/piece, and the fuse detection accuracy for 99.9%, the position recognition accuracy of the torque wrench is 100%.

Recognition of similar characters using gradient features of discriminative regions

One important and challenging issue in handwritten character recognition is the discrimination of visually similar characters. In this paper, we propose a character recognition method for distinguishing similar characters by augmenting commonly used image feature with gradient features from potentially discriminative image regions. The discriminative regions of similar characters sets are automatically detected by analysing the weight vectors of the sparsity promoting logistic fused Lasso method. The histogram of oriented gradients is adopted to compactly represent the gradient features. Additionally, the locality preserving projection method is employed to alleviate the high dimensional nature of the resulting feature vectors. Experimental results on handwritten Lanna Dhamma and Thai characters datasets demonstrate the capability of the proposed method in discriminating visually similar characters. The method also outperforms existing character recognition methods by considerable margins. It has a great potential for character recognition of other alphabets.

Online continuous multi-stroke Persian/Arabic character recognition by novel spatio-temporal features for digitizer pen devices

Nowadays, digitizer pens have become front end of many digital devices. The increasing use of this technology has necessitated the need for producing pen-based virtual keyboard systems. Despite attempts to create such systems in English, their absence for Persian/Arabic languages is an obvious defect. The goal of this paper is presenting an online continuous Persian/Arabic character recognition method. A character in Persian/Arabic language is made of two types of signs or strokes: main body and delayed strokes (which may be zero or more sign). In this paper, a set of novel and discriminative spatial features are defined for these strokes. These features then are used in a novel algorithm to create a genetic programming-based decision tree called GPDT. The GPDT and spatio-temporal features are utilized by non-deterministic finite automata (NDFA) to recognize group-related strokes and related characters. The reason for using spatio-temporal features is the sameness of the main body of some Persian/Arabic letters (e.g., “ح، خ، ج، چ”). There are also two other issues related to recognizing Persian/Arabic letters: unknown number of delayed stroke segments and the sameness of delayed strokes placement, which are removed by using an NDFA. In fact, after identifying group of main body with the help of GPDT, each recognized stroke makes a move in NDFA to stop in a character state (final state on the end of a path in NDFA). The proposed algorithm recognizes continuous Persian/Arabic letters and digits with a 92.43% accuracy and isolated letters and digits with 97.52% accuracy.

An efficient character recognition method using enhanced HOG for spam image detection

Generally, a spam image is an unsolicited message electronically sent to a wide group of arbitrary addresses. Due to attractiveness and more difficult detection, spam images are the most complicated type of spam. One of the ways to encounter the spam images is an optical character recognition, OCR, method. In this paper, the proposed enhanced HOG feature extraction method has been used so that the optical character recognition system of spam has been enhanced by using the HOG feature extraction method in such a way to be both resistant against the character variations on scale and translation and to be computationally cost-effective. For these purposes, two steps of the cropped image and input image size normalization have been added to pre-processing stages. Support vector machine, SVM, was employed for classification. Two heuristic modifications including thickening of the thin characters in the pre-processing stage and non-discrimination in detecting the uppercase and lowercase letters with the same shapes in the classification stage have been also proposed to increase the system recognition accuracy. In the first heuristic modification, when all pixels of the output image are empty (the character is eliminated), the original image was made thicker by one layer. In the second modification, when recognizing the letters, no differentiation was considered between the uppercase and lowercase letters with the same shapes. An average recognition accuracy of the modified HOG method with two heuristic modifications equals 91.61% on Char74K database. Then, an optimum threshold for classification was investigated by ROC curve. The optimal cutoff point was 0.736 with the highest average accuracy, 94.20%, and AUC, area under curve, for ROC and precision–recall, PR, curves were 0.96 and 0.73, respectively. The proposed method was also examined on ICDAR2003 database, and the average accuracy and its optimum using ROC curve were 82.73% and 86.01%, respectively. These results of recognition accuracy and AUC for ROC and PR curve showed an outstanding enhancement in comparison with the best recognition rate of the previous methods.

Recognition of Handwritten Chinese Characters Based on Concept Learning

Many deep-learning character recognition methods have been developed over the past few years. Chinese characters are widely used in many countries; however, the deep-learning-based Chinese character recognition methods are faced with various problems, such as a large amount of data required for training, numerous parameters, and a large consumption of computing resources. Concept learning is a hominine learning approach. Unlike existing deep-learning models, conceptual model learning can be realized by using as little as one sample. This paper is the first to propose a handwritten Chinese character recognition method based on concept learning. Different from the existing image representation-based character recognition methods, the proposed method builds a meta stroke library with prior knowledge, and then, presents a Chinese character conceptual model based on stroke relationship learning using a character stroke extraction method and Bayesian program learning. During character recognition, Monte Carlo Markov chain sampling is utilized to obtain the character generation model for each character conceptual. This generation model can calculate the probability of the target and training characters being the same classification, and thereby determines the classification of the target character. The experimental results indicate that, with the proposed method, the conceptual model of each character can be built for character classification prediction using as few as one character sample. Our approach obtains better performance than the state-of-the-art methods on ICDAR-2013 competition dataset.

An Online and Offline Character Recognition Using Image Processing Methods-A Survey

In this paper we are presenting different methods of character recognition that are present in nowadays. Character recognition can be performed by online and offline methods. We present a comparative study of all the methods available for the analysis of character recognition that implements image processing methods.