Intelligent system for assessing the harmfulness of food products based on the processing of textual and graphic information

2021 ◽  
Vol 26 (jai2021.26(2)) ◽  
pp. 27-40
Author(s):  
Sineglazov V ◽  
◽  
Kozak O ◽  
◽  
Keyword(s):  
Character Recognition ◽  
Intelligent System ◽  
Block Diagram ◽  
Food Composition ◽  
Input Image ◽  
Primary Sources ◽  
Food Labels ◽  
Mobile System ◽  
Classification Of Images ◽  
Composition Of Products

The paper substantiates the need to assess the harm of food for consumers with chronic diseases or allergies, which is important to prevent possible deterioration of the disease or eliminate acute allergic reactions of the human body to harmful ingredients present in the product. It is proved that currently there is no convenient intelligent system that could recognize the composition of products on the Ukrainian market, provide product characteristics and assess the harmfulness of the product. It is proposed to use food labels and packaging as primary sources of food information that is available to the consumer. It is shown that the printed information on the packages is presented in text-graphic form. The development of a mobile system as a software solution for the detection and analysis of textual and graphical information on the composition of products based on the use of artificial intelligence methods is proposed and substantiated. The block diagram of the intelligent mobile system for detection and analysis of food composition has been developed. The MSER algorithm is used to select text regions on the input image matrix in the presented algorithmic software. The solution to the problem of character recognition was based on the use of convolutional neural network MobileNet-V2, which is currently the best option in the classification of images by mobile applications that do not have a server part, and therefore large computing resources. Alignment of text on the image was carried out using the method of finding a rectangle with the smallest area Developed algorithms for grouping words. A decision support algorithm has been proposed to assess the harmfulness of products. The developed system allows personalized selection of food for each individual user, ie, the assessment of the composition of products is calculated taking into account the state of health of use, existing threats, diseases, restrictions or allergies

scholarly journals Consumer Knowledge about Food Labeling and Fraud

Foods ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1095
Author(s):  
Maria João Moreira ◽  
Juan García-Díez ◽  
José M. M. M. de Almeida ◽  
Cristina Saraiva
Keyword(s):  
Public Health ◽  
Meat Products ◽  
Principal Component ◽  
Food Composition ◽  
Food Labeling ◽  
Consumer Knowledge ◽  
Food Labels ◽  
Food Label ◽  
Food Fraud ◽  
Different Types

Food fraud is a growing problem and happens in many ways including mislabelling. Since lack of consumers’ knowledge about mandatory food labeling information and different types of food fraud may impact public health, the present work assesses consumers’ knowledge about these issues. Principal component analysis was performed to obtain a smaller number of uncorrelated factors regarding the usefulness and confidence of information displayed in food labels and the perception of food fraud. Results indicated that information displayed in food labels is useful, however the way it is presented may decrease consumer interest and understanding. Regarding respondents’ confidence in foodstuffs, over half of them stated that information provided in food labels is reliable. However, a lack of confidence about food composition is observed in those processed foodstuffs such as meat products. Food fraud is recognized by more than half of respondents with a higher perception of those practices that imply a risk to public health than those related to economic motivation. Age and education of consumers influenced the perception of the information displayed in the food labels, their confidence and knowledge about food fraud. Implementation of education programs to increase consumer knowledge about food labelling and fraud is essential. Respondents’ perception results could be use as guidelines by the food industry to improve food label design in order to enhance consumer understanding.

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

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

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

Probabilistic Model Based Hybrid Classifier for Character Recognition

2017 ◽  
Vol 25 (04) ◽  
pp. 621-647
Author(s):  
A. K. Sampath ◽  
N. Gomathi
Keyword(s):  
Probabilistic Model ◽  
Character Recognition ◽  
Recognition Rate ◽  
Size Variation ◽  
Orientation Angle ◽  
Recognition System ◽  
Input Image ◽  
Hybrid Classifier ◽  
Form Processing ◽  
Model Based

Handwritten character recognition is most crucial one indulging in many of the applications like forensic search, searching historical manuscripts, mail sorting, bank check reading, tax form processing, book and handwritten notes transcription etc. The problem occurrence in the recognition is mainly because of the writing style variation, size variation (length and height), orientation angle etc. In this paper a probabilistic model based hybrid classifier is proposed for the character recognition combining the neural network and decision tree classifiers. In addition to the local gradient features i.e. histogram oriented feature and grid level feature, an additional feature called GLCM feature is extracted from the input image in the proposed recognition system and are concatenated for the image recognition procedure to encode color, shape, texture, local as well as the statistical information. These extracted features considered are given to the hybrid classifier which recognises the character. In the test set, recognition accuracy of 95% is achieved. The proposed probabilistic model based hybrid classifier tends to contribute more accurate character recognition rate compared to the existing character recognition system.

scholarly journals Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric

2018 ◽  
Vol 3 (7) ◽  
pp. 67
Author(s):  
Mousumi Hasan Mukti ◽  
Quazi Saad-Ul-Mosaher ◽  
Khalil Ahammad
Keyword(s):  
Pattern Recognition ◽  
Fourier Transform ◽  
Character Recognition ◽  
Euclidean Distance ◽  
Great Part ◽  
Research Area ◽  
Input Image ◽  
Handwritten Character ◽  
Input Dataset ◽  
Implementation Tool

Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800 handwritten character texts from different people has been developed for this purpose and these character texts are converted to their equivalent printed version to implement this research. MATLAB has been used as an implementation tool for different preprocessing techniques like cropping, resizing, flood filling, thinning etc. Processed text images are used as input to the system and they are converted to FT. Handwritten character of different person may be of different style and angle. The input dataset is collected from various types of people including age level from 5 to 70 years, from different professions like pre-schooling students, graduate students, doctors, teachers and housewives. So, to match the input image with printed dataset (PDS) each printed data is rotated up to 450 left and right and then their FT is computed. The Euclidean distance among the input image and the rotated 30 images of each printed text are taken as intermediate distance set. The minimum value of Euclidean distance for a character is used to recognize the targeted character from the intermediate set. Wrongly detected texts are not thrown away from the system rather those are stored in the named character or digits file so that those can be used in future for deep learning. By following the proposed methodology, the research has achieved 98.88% recognition accuracy according to the input and PDS.

scholarly journals Character Segmentation and Recognition of Marathi Language

2021 ◽  
Vol 9 (12) ◽  
pp. 1544-1551
Author(s):  
Saurabh Ravindra Nikam
Keyword(s):  
Image Segmentation ◽  
Character Recognition ◽  
Structural Features ◽  
Input Image ◽  
Character Segmentation ◽  
Important Process ◽  
Critical Task ◽  
The Individual ◽  
Segmentation Image

Abstract: In this paper Segmentation is one the most important process which decides the success of character recognition fashion. Segmentation is used to putrefy an image of a sequence of characters into sub images of individual symbols by segmenting lines and words. In segmentation image is partitioned into multiple corridor. With respect to the segmentation of handwritten words into characters it's a critical task because of complexity of structural features and kinds in writing styles. Due to this without segmentation these touching characters, it's delicate to fete the individual characters, hence arises the need for segmentation of touching characters in a word. Then we consider Marathi words and Marathi Numbers for segmentation. The algorithm is use for Segmentation of lines and also characters. The segmented characters are also stores in result variable. First it Separate the lines and also it Separate the characters from the input image. This procedure is repeated till end of train. Keywords: Image Segmentation, Handwritten Marathi Characters, Marathi Numbers, OCR.

An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture

2021 ◽  
pp. 435-446
Author(s):  
Debanjan Konar ◽  
Suman Kalyan Kar
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Input Image ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Fuzzy Input ◽  
Feed Forward Network ◽  
Network Output ◽  
Fuzziness Measure ◽  
Neighborhood Topology

This chapter proposes a quantum multi-layer neural network (QMLNN) architecture suitable for handwritten character recognition in real time, assisted by quantum backpropagation of errors calculated from the quantum-inspired fuzziness measure of network output states. It is composed of three second-order neighborhood-topology-based inter-connected layers of neurons represented by qubits known as input, hidden, and output layers. The QMLNN architecture is a feed forward network with standard quantum backpropagation algorithm for the adjustment of its weighted interconnection. QMLNN self-organizes the quantum fuzzy input image information by means of the quantum backpropagating errors at the intermediate and output layers of the architecture. The interconnection weights are described using rotation gates. After the network is stabilized, a quantum observation at the output layer destroys the superposition of quantum states in order to obtain true binary outputs.

scholarly journals License plate identification and recognition in a non-standard environment using neural pattern matching

2021 ◽  
Author(s):  
Imran Shafi ◽  
Imtiaz Hussain ◽  
Jamil Ahmad ◽  
Pyoung Won Kim ◽  
Gyu Sang Choi ◽  
...  
Keyword(s):  
Character Recognition ◽  
Detection Algorithm ◽  
Input Image ◽  
Recognition Algorithm ◽  
Detection Accuracy ◽  
License Plate ◽  
Recognition Mechanism ◽  
Robust Detection ◽  
Feature Representations ◽  
Recognition Efficiency

AbstractNon-standard license plates are a part of current traffic trends in Pakistan. Private number plates should be recognized and, monitored for several purposes including security as well as a well-developed traffic system. There is a challenging task for the authorities to recognize and trace the locations for the certain number plate vehicle. In a developing country like Pakistan, it is tough to have higher constraints on the efficiency of any license plate identification and recognition algorithm. Character recognition efficiency should be a route map for the achievement of the desired results within the specified constraints. The main goal of this study is to devise a robust detection and recognition mechanism for non-standard, transitional vehicle license plates generally found in developing countries. Improvement in the character recognition efficiency of drawn and printed plates in different styles and fonts using single using multiple state-of-the-art technologies including machine-learning (ML) models. For the mentioned study, 53-layer deep convolutional neural network (CNN) architecture based on the latest variant of object detection algorithm-You Only Look Once (YOLOv3) is employed. The proposed approach can learn the rich feature representations from the data of diversified license plates. The input image is first pre-processed for quality improvement, followed by dividing it into suitable-sized grid cells to find the correct location of the license plate. For training the CNN, license plate characters are segmented. Lastly, the results are post-processed and the accuracy of the proposed model is determined through standard benchmarks. The proposed method is successfully tested on a large image dataset consisting of eight different types of license plates from different provinces in Pakistan. The proposed system is expected to play an important role in implementing vehicle tracking, payment for parking fees, detection of vehicle over-speed limits, reducing road accidents, and identification of unauthorized vehicles. The outcome shows that the proposed approach achieves a plate detection accuracy of 97.82% and the character recognition accuracy of 96%.

RECOGNITION OF HANDWRITTEN SIMILAR CHINESE CHARACTERS BY SELF-GROWING PROBABILISTIC DECISION-BASED NEURAL NETWORK

1999 ◽  
Vol 09 (06) ◽  
pp. 545-561 ◽  
Author(s):  
HSIN-CHIA FU ◽  
Y. Y. XU ◽  
H. Y. CHANG
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Difficult Problem ◽  
Input Image ◽  
Prototype System ◽  
Credit Assignment ◽  
Similar Character

Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters × 200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance.5,11 As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.

scholarly journals A Bilingual Numeral OCR System for Creating Uni-Lingual Digitized Numeral Document

2015 ◽  
Vol 9 (13) ◽  
pp. 148 ◽  
Author(s):  
Karthick K ◽  
Chitra S
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Input Image ◽  
Self Organizing Map ◽  
Text Documents ◽  
Library Automation ◽  
Text Document ◽  
Billing System ◽  
Postal Automation

<p>The optical character recognition has been used in many applications such as dictionary generation, customer billing system, banking and postal automation, and library automation etc. The bilingual OCR system to make uni-lingual script helps us to reduce the requirement of two different OCR systems into a single OCR system for recognition of two different languages. This type of globalization helps the universal users of any language can read the text documents in their self-language if the bilingual documents are converted into uni-lingual document. In this paper, the image which contains printed Tamil and European numerals has been recognized using common OCR System and the Tamil numerals are converted into European numerals to globalize the document from a bilingual script into a uni-lingual document. The main objective of the work is to bring out the single numeral (European numerals) text document from the input image with two different numerals (Tamil and European Numerals). The Kohonen’s self-organizing map (SOM) based recognition system has been used for recognizing the numerals and recognized characters in bilingual numerals (Tamil and European Numerals) form are converted into Uni-lingual form (European numerals). This paper also discusses the various approaches used for OCR.</p>

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