Freeman Chain Code Research Articles

Feature Extraction Algorithm based Metaheuristic Optimization for Handwritten Character Recognition

Interest in feature extraction for Handwritten Character Recognition (HCR) has been growing due to numerous algorithms aimed at improving classification accuracy. This study introduces a metaheuristic approach utilizing the Honey Badger Algorithm (HBA) for feature extraction in HCR. The Freeman Chain Code (FCC) is employed for data representation. One challenge with using FCC to represent characters is that extraction results vary depending on the starting points, affecting the chain code's route length. To address this issue, a metaheuristic approach using HBA is proposed to identify the shortest route length and minimize computational time for HCR. The performance metrics of the HB-FCC extraction algorithm are route length and computation time. Experiments on the algorithm use chain code representations from the Center of Excellence for Document Analysis and Recognition (CEDAR) dataset, containing 126 uppercase letter characters. According to the results, the proposed HB-FCC method achieves a route length of 1880.28 and requires only 1.07 seconds to process the entre set of character images.

Intelligent Vehicle Inspection Tool Design Based on Freeman Chain Code for Automatic Annotation of 3D Models

Autonomous vehicle are more and more widely used in daily life, and the requirements for their safety performance are higher and higher. As a tool for testing auto parts, intelligent inspection tools are crucial to the guarantee of automobile quality. However, traditional fixture design relies on manual drawing, which is inefficient and prone to errors. To solve this problem, this research uses Freeman chain code to determine the annotation object, uses case clustering method to annotate, and uses error back propagation algorithm to realize case knowledge classification learning, and designs intelligent vehicle inspection tool design technology based on Freeman chain code 3D automatic annotation method. The experimental results show that the geometric feature matching results are correct, and the difference in feature comparison results is significant, with a high accuracy rate. Meanwhile, the geometric similarity annotation method has a high accuracy rate, taking only 3 minutes to complete the annotation, which is 7 minutes longer than traditional manual annotation. The error backpropagation algorithm can accurately achieve feature classification, and the design time of size chain inspection tool deformation design is reduced by 214 minutes compared to manual reverse deformation design, significantly improving design efficiency. In summary, the proposed design method for automotive inspection tools can achieve automatic model annotation, improve design efficiency, and reduce design time.

Binocular measurement method for a continuous casting slab based on the one-dimensional probabilistic Hough transform and local sub-pixel sifting.

Due to the low measurement accuracy of the continuous casting slab model caused by difficulty in detecting ideal corners, a binocular measurement method based on the one-dimensional probabilistic Hough transform and local sub-pixel sifting is proposed. First, the one-dimensional probabilistic Hough transform based on inclination angle voting and the Freeman chain code is used to detect the line segments of the exterior outline. Next, sub-pixel points are extracted in each region of interest (ROI) by using Zernike moments, and sifted in the overlapping area of adjacent ROIs. Then the orthogonal total least squares (TLS) method is applied to fitting sub-pixel edges. Finally, after the key points are matched, three-dimensional localization and measurement are completed according to the binocular vision measurement principle. The experimental results show that the minimum relative error and average relative error of length reach 0.3401% and 0.3945%, respectively, satisfying the measurement requirement. Compared with scale-invariant feature transform (SIFT) and the oriented FAST and rotated BRIEF (ORB), the measurement error of the proposed algorithm is reduced by 80.01% and 74.63%, respectively. Compared with another edge fitting method based on k-means clustering and least squares fitting, its measurement error is reduced by 34.11%, and the time consumption is shortened by 39.07%, verifying its excellent performance in accuracy and efficiency.

A Progressive Approach to Arabic Character Recognition Using a Modified Freeman Chain Code Algorithm

Arabic character identification presents a significant obstacle to the comprehension and analysis of Arabic text. This paper presents an improved technique that generates Freeman code from handwritten Arabic characters. This code provides the shortest code length without losing character information, accounting for all handwritten Arabic character variants. We tested this code using a set of Arabic characters in various formats to identify Arabic characters in order to take use of the code generated by our enhanced method. We also performed a comparison between our Freeman code and codes generated in other related research. In light of this, the code that we obtained correctly represents the Arabic letter in all of its variants, including the ones that the algorithms in previous publications did not consider. Consequently, our novel method based on Freeman coding represents a significant advancement in Arabic character recognition. Furthermore, our method provides a successful way of identifying and presenting Arabic characters

Encoding the Intensity and Phase Gradient of Light Beams with Arbitrary Shapes

We present an approach for engineering the intensity trajectory and phase gradient of light beams with arbitrary shapes by estimating their parametric equations using Freeman chain code and by applying the fast Fourier transform. The analysis of the electric field distribution expected for a given curve allows the phase extraction over each local coordinate, generating a phase pattern to be displayed over a spatial light modulator. The intensity and phase gradient of eight different shapes is encoded during our experiments. The far field intensity profiles are captured and compared in shape to those designed, while the encoded phase is demonstrated by implementing a common path interference setup with a pair of beams from the spatial light modulator. The designed beams, initially drawn either by hand or generated with software, exhibit both the intensity and phase profiles encoded onto them.

Improved Classification of Handwritten Jawi Script Based on Main Part of Script Body

Since the entry of Islam, many ancient relics in the archipelago were written using Jawi script. Due to human or natural factors, these ancient relics will be damaged or destroyed. To avoid the loss of this ancient heritage data, the data must be stored in digital documents. In order to convert digital documents into machine-readable text format, the use of Optical Character Recognition (OCR) technology is inevitable. In this research, OCR technology is implemented on isolated Jawi scripts. Freeman Chain Code (FCC) is used to extract the isolated Jawi script features. Subsequently, the FCC feature is fed into Support Vector Machine (SVM) in order to classify the character. The decision rule classification is applied to the class of SVM classification in the Jawi script form. The results of the SVM classification into 19 classes reached 81.58%, while the results for merging into 15 classes produced better results with the accuracy 84.21%. Feature extraction of dot location is divided into the top, middle, and bottom. Feature extraction of the number of dotss is done by counting the number of dots, while feature extraction of the presence of holes is carried out by detecting the presence of holes in the characters. These features are applied to the class of results from SVM classification with decision-making rules. The percentage of success in applying the decision rules to the results of the classification of incorporation into 15 classes by SVM reached 92.86%. Further research will be conducted to determine the effect of the feature of the location of the dot and the number of dots on the shape of the main part of the character.

Recognition of Kannada Character Scripts Using Hybrid Feature Extraction and Ensemble Learning Approaches

An automated handwritten script identification system seeks more attention in the academic research field and commercial applications. Recognizing the handwritten Kannada scripts in recent years is an active research area. But, it is a much more challenging one in the pattern recognition field owing to the complexity of structural hierarchy, huge vocabulary count, and distinct people’s diverse handwriting styles. Therefore, this paper aims to develop Kannada handwritten script recognition framework by ensemble methods. Initially, the essential Kannada handwritten text images are collected. These collected images are used for pre-processing with the CLAHE and filtering techniques and followed by segmentation with the active contour approach. The segmented images are utilized using the Adaptive Local Tetra Pattern with freeman chain code histogram techniques. Here, the parameters in the LTrP are tuned with Hybrid Honey Badger Henry Gas Solubility Optimization. Finally, the handwritten recognition is performed by Hybrid Feature Extraction with Ensemble Deep Learning (HFE-EDH). Further, the recognition rate is elevated with the parameter optimization in deep learning approaches using the HGSO + HBA algorithm. Throughout the result analysis, the accuracy of the designed method attains 96%. Thus, the proposed method reveals better performance regarding various performance measures.

HPKNN: Hyper‐parameter optimized KNN classifier for classification of poikilocytosis

AbstractRed blood cells (RBCs) are the more crucial components of a healthy organism. Poikilocytosis is a disorder in which blood cells lose their standard shapes. The human bodies are affected by disorders like anemia and thalassemia because the structure of RBCs shape changes. Hematologists take longer time to detect abnormalities in RBCs. The proposed hyper‐parameter optimizable K nearest neighbor (HPKNN) algorithm detects and classifies numerous types of poikilocytosis quickly and accurately. On the Chula‐PIC‐Lab and Erythrocytes IDB datasets, the proposed HPKNN algorithm was tested. After the preprocessing stage, the Freeman chain code has been utilized to retrieve the exact boundary shape of RBC morphology. On datasets of Erythrocytes IDB 1, 2, 3, and Chula‐PIC‐Lab(Chula‐RBC‐12), the morphological tools and Freeman chain code based segmentation provide 97.44% and 94.45% accuracy, respectively, which is superior than other methods. The proposed HPKNN classifier has an overall sensitivity of 97.9%, precision of 97.9%, and an f1 score of 97.9% on the Chula‐PIC‐Lab (Chula‐RBC‐12) dataset to classify 10 classes, namely acanthocyte, dacrocyte, echinocyte, elliptocyte, normal, overlapped, schistocyte, spherocyte, stomatocyte, and target cell, which is greater than any other current approach. The HPKNN classifier achieves 98.085% precision, 98.085% sensitivity or recall, and 98.085% f1‐score values on the Erythrocytes IDB datasets to classify circular, elongated, and other RBC classes.

Implementasi Algoritma Freeman Chain Code dan Algoritma K-Nearest Neighbor Dalam Pengenalan Huruf Mandarin

Mandarin is the second international language used by the world's population and is the language most studied by students in Indonesia. This research was conducted to determine the success rate of the Freeman Chain Code algorithm and the K-Nearest Neighbor algorithm in mandarin letter recognition. In recognizing Chinese characters, there are several stages that must be passed, namely pre-processing, feature extraction, and letter recognition. The pre-processing stage uses grayscale, Gaussian Blur, binaryization, and thinning. The feature extraction stage uses the Freeman Chain Code Algorithm and the Depth First Search (DFS) Algorithm. The classification stage uses the K-Nearest Neighbor Algorithm and the L1-Metric Algorithm (Manhattan Distance). In this study, there were 10 letter classes with each letter having 100 sample images. The distribution ratio of this research is 70% training data and 30% testing data. This research produces an application that is able to recognize Chinese characters. The success rate resulting from this study was 72% with 216 of the 300 images successfully recognized

Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image

The lesions of COVID-19 CT image show various kinds of ground-glass opacity and consolidation, which are distributed in left lung, right lung or both lungs. The lung lobes are uneven and it have similar gray value to the surrounding arteries, veins, and bronchi. The lesions of COVID-19 have different sizes and shapes in different periods. Accurate segmentation of lung parenchyma in CT image is a key step in COVID-19 detection and diagnosis. Aiming at the unideal effect of traditional image segmentation methods on lung parenchyma segmentation in CT images, a lung parenchyma segmentation method based on two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm is proposed. Firstly, the optimal threshold method is used to realize the initial segmentation of the lung, so that the segmentation threshold can change adaptively according to the detailed information of lung lobes, trachea, bronchi and ground-glass opacity. Then the lung parenchyma is further processed to obtain the lung parenchyma template, and then the defective template is repaired combined with the improved Freeman chain code and Bezier curve. Finally, the lung parenchyma is extracted by multiplying the template with the lung CT image. The accuracy of lung parenchyma segmentation has been improved in the contrast clarity of CT image and the consistency of lung parenchyma regional features, with an average segmentation accuracy rate of 97.4%. The experimental results show that for COVID-19 and suspected cases, the method has an ideal segmentation effect, and it has good accuracy and robustness.

License Plate Character Recognition using Convolutional Neural Network

Background: In the last decade, the number of registered vehicles has grown exponentially. With more vehicles on the road, traffic jams, accidents, and violations also increase. A license plate plays a key role in solving such problems because it stores a vehicle’s historical information. Therefore, automated license-plate character recognition is needed. Objective: This study proposes a recognition system that uses convolutional neural network (CNN) architectures to recognize characters from a license plate’s images. We called it a modified LeNet-5 architecture. Methods: We used four different CNN architectures to recognize license plate characters: AlexNet, LeNet-5, modified LeNet-5, and ResNet-50 architectures. We evaluated the performance based on their accuracy and computation time. We compared the deep learning methods with the Freeman chain code (FCC) extraction with support vector machine (SVM). We also evaluated the Otsu and the threshold binarization performances when applied in the FCC extraction method. Results: The ResNet-50 and modified LeNet-5 produces the best accuracy during the training at 0.97. The precision and recall scores of the ResNet-50 are both 0.97, while the modified LeNet-5’s values are 0.98 and 0.96, respectively. The modified LeNet-5 shows a slightly higher precision score but a lower recall score. The modified LeNet-5 shows a slightly lower accuracy during the testing than ResNet-50. Meanwhile, the Otsu binarization’s FCC extraction is better than the threshold binarization. Overall, the FCC extraction technique performs less effectively than CNN. The modified LeNet-5 computes the fastest at 7 mins and 57 secs, while ResNet-50 needs 42 mins and 11 secs. Conclusion: We discovered that CNN is better than the FCC extraction method with SVM. Both ResNet-50 and the modified LeNet-5 perform best during the training, with F measure scoring 0.97. However, ResNet-50 outperforms the modified LeNet-5 during the testing, with F-measure at 0.97 and 1.00, respectively. In addition, the FCC extraction using the Otsu binarization is better than the threshold binarization. Otsu binarization reached 0.91, higher than the static threshold binarization at 127. In addition, Otsu binarization produces a dynamic threshold value depending on the images’ light intensity. Keywords: Convolutional Neural Network, Freeman Chain Code, License Plate Character Recognition, Support Vector Machine

The method of quantitative assessment of posttraumatic spinal cord injury volume and structure in animal models using magnetic resonance tomography

Introduction. Spinal cord injury is a debilitating traumatic event in central nervous system resulting in tissue destruction and severe neurological deficit development. Preclinical assessment of quantitative lesion area parameters (e. g. structure and volume) is critical for subsequent evaluation of neuroprotective and / or neuroregenerative therapy efficiency. Current methods for parameter calculation require manual limitation of the interested area (region of interest, RoI). This process is tedious and often not precise enough.Study objective is to develop and implement software for automated assessment of volume and structure of posttraumatic spinal cord lesion using extra-high-field MRI 7.0 Tesla and to compare methods preciseness with the current manual techniques.Study design. Ten rat models of acute severe spinal cord contusion injury were used including female Sprague–Dawley animals weighting 250–350 gr. MRI imaging was performed in 1 day postoperative and then 4 times with interval (1 week). Study was prospective open-label uncontrolled comparative.Materials and methods. Standard spinal cord contusion injury model was used. Anesthetized animals underwent laminectomy at level Th9–Th10 vertebrae followed by “weight drop” injury technique application: 10 g weight with 2 mm pin diameter dropped from 25 mm height. Software was developed using Microsoft Visual Studio 2017 environment and programming language C#. Statistical analysis was performed using IBM SPSS Statistics 21.0 software.Results. We developed and patented specialized software Spinal cavity Searcher realizing the algorithm of T2‑weighted images (T2‑WI) analysis based on image bynarization and Freeman chain code. This algorithm supports calculation of spinal cord posttraumatic lesion parameters in a half-automatic manner. Results of this algorithm application were comparable to results of manual calculation: no statistical difference were observed between two values.Conclusion. Current method of spinal cord injury volume and structure quantitative assessment simplifies the calculation procedure due to automatization of RoI limitation comparing to manual technique. The level of preciseness is comparable in both methods.Clinical relevance. The developed algorithm optimizes the process of non-invasive control of the performed treatment efficiency according to 7.0 Tesla MRI data.

An Ancient Number Recognition using Freeman Chain Code with Deep Learning Approach

Sanskrit character and number documents have a lot of errors. Correcting those errors using conventional spell-checking approaches breaks down due to the limited vocabulary. This is because of high inflexions of Sanskrit, where words are dynamically formed by Sandhi rules, Samasa rules, Taddhita affixes, etc. Therefore, correcting OCR documents require huge efforts. Here, we can present different machine learning approaches and various ways to improve features for ameliorating the error corrections in Sanskrit documents. Simulation of Sanskrit dictionary for synthesizing off-the-shelf dictionary can be done. Most of the proposed methods can also work for general Sanskrit word corrections and Hindi word corrections. Handwriting recognition in Indic scripts, like Devanagari, is very challenging due to the subtitles in the scripts, variations in rendering and the cursive nature of the handwriting. Lack of public handwriting datasets in Indic scripts has long stymied the development of offline handwritten word recognizers and made comparison across different methods a tedious task in the field. In this paper, a new handwritten word dataset will be released for Devanagari, IIIT-HW-Dev to alleviate some of these issues. This process is required for successful training of deep learning architecture, availability of huge amounts of training data is crucial, as any typical architecture contains millions of parameters. A new method for the classification of freeman chain code using four-connectivity and eight-connectivity events with deep learning approach is presented. Application of CNN LeNet-5 is found to be suitable to get results in this cases as the numbers are formed with curved lines In contrast with the existing FCC event data analysis techniques, sampled grey images of the existing events are not used, but image files of the three-phase PQ event data are analysed by taking the advantage of the success of the deep learning approach on imagefile-classification. Therefore, the novelty of the proposed approach is that image files of the voltage waveforms of the three phases of the power grid are classified. It is shown that the test data can be classified with 100% accuracy. The proposed work is believed to serve the needs of the future smart grid applications, which are fast and taking automatic countermeasures against potential PQ events.

Chainlet-Based Ear Recognition Using Image Multi-Banding and Support Vector Machine

This paper introduces the Chainlet-based Ear Recognition algorithm using Multi-Banding and Support Vector Machine (CERMB-SVM). The proposed technique splits the gray input image into several bands based on the intensity of its pixels, similar to a hyperspectral image. It performs Canny edge detection on each generated normalized band, extracting edges that correspond to the ear shape in each band. The generated binary edge maps are then combined, creating a single binary edge map. The resulting edge map is then divided into non-overlapping cells and the Freeman chain code for each group of connected edges within each cell is determined. A histogram of each group of contiguous four cells is computed, and the generated histograms are normalized and linked together to create a chainlet for the input image. The created chainlet histogram vectors of the images of the dataset are then utilized for the training and testing of a pairwise Support Vector Machine (SVM). Results obtained using the two benchmark ear image datasets demonstrate that the suggested CERMB-SVM method generates considerably higher performance in terms of accuracy than the principal component analysis based techniques. Furthermore, the proposed CERMB-SVM method yields greater performance in comparison to its anchor chainlet technique and state-of-the-art learning-based ear recognition techniques.

Short FCC and CCCto Detect Eyes and Mouth in Dofferent Image Size

The face parts that can uses for recognition e.g eyes, mouth, nose, etc. Most of the face recognition did resampling the image to the same size. This research aim to regconize the eyes and mouth in the image with different size. For the features extraction algorithm that use is short Freman Chain Code and for the recognition use Consecutive Changing Characters algorithm. Data that use to be data training is the images that have the different size. The result of experiment shows that using adaptive Freeman Chain Code algorithm and adaptive Consecutive Changing Characters algorithm, the percentage of success rate is 60% for eyes and 75% for mouth.

IMPLEMENTASI ZONING DAN FITUR ARAH SEBAGAI EKSTRAKSI FITUR PADA PENGENALAN TULISAN TANGAN AKSARA BALI

Character recognition is one of the most researched fields in computer science. Combining the field of digital image processing and pattern recognition is a challenge in determining the most optimal method combination to complete character recognition. Balinese script is one of the regional scripts used in Balinese literary. The challenge with Balinese script is that some of its characters have a degree of similarity. So far, several methods of feature extraction that have been studied for Balinese script are modified direction feature, template matching, image centroid zone and zone centroid zone, local binary pattern. In this research, we combine methods based on zoning and directional features. The methods used are ICZ, ZCZ and freeman chain code to find the characteristics of Balinese script handwriting. The addition of chain code method aims to determine the value around the foreground point. The results of feature extraction will be used as input in the Support Vector Machine for the classification process. The test result shows that the combination of the ICZ, ZCZ and freeman chain code methods produces an accuracy of 89.09%, while the combination of ICZ and ZCZ produces 88.06% of accuracy. The SVM kernels compared use linear kernels.

Automatic handwritten character recognition of Devanagari language: a hybrid training algorithm for neural network

In the field of image processing and Artificial Intelligence the character recognition and handwritten recognition have been emerging as one of the active and challenging research areas. In recent years, there has been a notable development in the research associated with character recognition in handwritten Devanagari documents. To improve the recognition performance, this paper tactics to develop new handwritten character recognition model using an improved machine learning approach. The proposed character recognition model includes four stages like pre-processing, Segmentation, Feature Extraction, and Classification. Initially, the scanned handwritten document for Devanagari language is subjected to pre-processing, which includes stages like, RGB to gray, thresholding, complement of image, morphological operations, linearization and noise removal using Median Filter. Then, the characters of the pre-processed image are segmented using k-means clustering that is a popular method for cluster analysis. Further, the features like, Kirsch Directional Edge, Freeman chain code and neighborhood distance weight using Delaunay Triangulation are extracted from the segmented characters. Subsequently, the classification of characters is done using Neural Network (NN) where the new training algorithm is used, in which the weights are optimized using a hybrid optimization by combining Lion Optimization Algorithm and Grey Wolf Optimization (GWO). Here, the update procedure of GWO is based on LA algorithm and hence, the proposed algorithm is named as Lion Updated GWO (LU-GWO). To the next of the implementation, a valuable comparative analysis confirms the improved performance of the proposed LU-GWO-NN model over conventional methods in classifying the consonants, numerals, and vowels from different characters.

Online fault diagnosis for sucker rod pumping well by optimized density peak clustering

Online diagnosis for sucker rod pumping well has great significances for rapidly grasping operations of the oil well. Feature extraction of the working condition and determination of the online diagnostic algorithm are two indispensable parts. In this paper, five feature vectors are extracted using Freeman chain codes. Then, an optimized density peak clustering (DPC) method is proposed to realize online diagnosis solved by an improved brain storm optimization (BSO) algorithm, in which the cloud model is adopted to generate new solutions in the searching space. During the online diagnosis process, a new cluster updating strategy is used to update the cluster centers online. According to the proposed online diagnostic method, various samples are automatically classified into different classifications by the unsupervised learning. The simulation results verify that the proposed online diagnosis method is satisfactory, which can give a higher and more stable diagnostic accuracy.

Lossless chain code compression with an improved Binary Adaptive Sequential Coding of zero-runs

A new method for encoding a sequence of integers, named Binary Adaptive Sequential Coding with Return to Bias, is proposed in this paper. It extends the compressing pipeline for chain codes’ compression consisting of Burrows Wheeler Transform, Move-To-Front Transform, and Adaptive Arithmetic Coding. We also explain when to include the Zero-Run Transform into the above-mentioned pipeline. The Zero-Run Transform generates a sequence of integers corresponding to the number of zero-runs. This sequence is encoded by Golomb coding, Binary Adaptive Sequential Coding, and the new Binary Adaptive Sequential Coding with Return to Bias. Finally, a comparison is performed with the two state-of-the-art methods. The proposed method achieved similar compression efficiency for the Freeman chain code in eight directions. However, for the chain codes with shorter alphabets (Freeman chain code in four directions, Vertex Chain Code, and Three-OrThogonal chain code), the introduced method outperforms the referenced ones.

Comparing Freeman Chain Code 4 Adjacency Algorithm and LZMA Algorithm in Binary Image Compression

Image is one product that has experienced a significant increase in quality. Insufficient internet capacity makes it difficult to access large images. Image compression is one way to solve this problem, but image compression can make image quality degrade. This research uses lossless compression algorithms, namely Chain Code Algorithm and LZMA Algorithm, to reduce the quality of the image and can be adjusted the tolerance value manually. In the application that will be created, the user can select the image to be compressed, then compress it according to the tolerance value that has been set and with the compression result of a text file, where the text file can be extracted and reconstructed into an image. There will be a preview of the results of the image compression and a description of the size and quality reduction levels. The conclusion of this research is that Chain Code Algorithm and LZMA Algorithm are able to compress image file sizes / images with extensions .jpg, .png. .bmp with a success rate of 90% from 50 tests.