An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging

There exist various types of information in retail food packages, including food product name, ingredients list and use by date. The correct recognition and coding of use by dates is especially critical in ensuring proper distribution of the product to the market and eliminating potential health risks caused by erroneous mislabelling. The latter can have a major negative effect on the health of consumers and consequently raise legal issues for suppliers. In this work, an end-to-end architecture, composed of a dual deep neural network based system is proposed for automatic recognition of use by dates in food package photos. The system includes: a Global level convolutional neural network (CNN) for high-level food package image quality evaluation (blurry/clear/missing use by date statistics); a Local level fully convolutional network (FCN) for use by date ROI localisation. Post ROI extraction, the date characters are then segmented and recognised. The proposed framework is the first to employ deep neural networks for end-to-end automatic use by date recognition in retail packaging photos. It is capable of achieving very good levels of performance on all the aforementioned tasks, despite the varied textual/pictorial content complexity found in food packaging design

Digital image processing in the creation of an intelligent system prototype for text detection and recognition in the labeling process of electrical cable

El etiquetado del cable permite identificar distintas configuraciones y lotes de cables, así como conocer sus características. En el proceso de etiquetado del cable se pueden presentar distintos tipos de errores o defectos en el texto impreso, por ejemplo: ausencia de la etiqueta, corrimiento de la tinta, partes del texto faltante, texto ilegible y gotas de tinta. En este trabajo se presenta un prototipo de sistema para la validación del texto en cables eléctricos mediante técnicas de procesamiento de imágenes. El sistema propuesto integra dos etapas. En la primera etapa se propuso un método basado en clusterización por K-means que permite realizar el preprocesado de las imágenes para acondicionarlas. En la segunda etapa se realiza el reconocimiento óptico de caracteres utilizando el motor Tesseract OCR, el cual permite convertir el texto contenido en las imágenes en cadenas de caracteres. La experimentación se realizó utilizando una colección de 909 imágenes de cables eléctricos que contiene ocho tipos diferentes de cables, donde las imágenes están etiquetadas individualmente con la ground truth. La experimentación realizada permitió obtener una tasa de error promedio del 6.54%, 3.97% y 2.53% al validar el texto empleando tres, cinco y siete secuencias de etiquetas, respectivamente.Cable labeling allows identifying different configurations and batches of cables, as well as their characteristics. In the cable labeling process, different types of errors or defects can occur in the printed text, such as the absence of the label, ink bleeding, text parts missing, illegible text, and ink drops. In this paper, a prototype of a system for text validation in electrical cables using image processing techniques is presented. The proposed system consists of two stages. In the first stage, a method based on K-means clustering was proposed, allowing preprocessing images to condition them. In the second stage, optical character recognition using the Tesseract OCR engine is performed, allowing the text in the images to be converted into character strings. The experimentation was carried out using a collection of 909 images of electrical cables containing eight different types of cables, where the images are individually labeled with the ground truth. The evaluation obtained an average error rate of 6.54%, 3.97%, and 2.53% when validating the text using three, five, and seven label sequences, respectively

Recognition and classification: the use of computer vision in the retail industry

Project work presented as a partial requisite to obtain the Master Degree in Information Management with specialization in Knowledge Management and Business intelligenceAutomatic recognition of text and classification of it, using image processing techniques such as optical character recognition and machine learning, are indicating new ways of capturing information on fast-moving consumer goods. Such systems can play an important role in market research processes and operations, in being more efficient and agile. The necessity is to create a system that is able to extract all text available on the packaging and quickly arrange it into attributes. The goal of this investigation is to use a combination of optical character recognition and machine learning to achieve a satisfactory level of efficiency and quality. In order for such a system to be introduced to the organization, it needs to be faster and more effective than currents process. One of the advantages of using such a system is the independence of the human factor, which leads to a higher probability of error

A systematic literature review on machine learning applications for sustainable agriculture supply chain performance

Agriculture plays an important role in sustaining all human activities. Major challenges such as overpopulation, competition for resources poses a threat to the food security of the planet. In order to tackle the ever-increasing complex problems in agricultural production systems, advancements in smart farming and precision agriculture offers important tools to address agricultural sustainability challenges. Data analytics hold the key to ensure future food security, food safety, and ecological sustainability. Disruptive information and communication technologies such as machine learning, big data analytics, cloud computing, and blockchain can address several problems such as productivity and yield improvement, water conservation, ensuring soil and plant health, and enhance environmental stewardship. The current study presents a systematic review of machine learning (ML) applications in agricultural supply chains (ASCs). Ninety three research papers were reviewed based on the applications of different ML algorithms in different phases of the ASCs. The study highlights how ASCs can benefit from ML techniques and lead to ASC sustainability. Based on the study findings an ML applications framework for sustainable ASC is proposed. The framework identifies the role of ML algorithms in providing real-time analytic insights for pro-active data-driven decision-making in the ASCs and provides the researchers, practitioners, and policymakers with guidelines on the successful management of ASCs for improved agricultural productivity and sustainability