Identification of Alphanumeric Pattern Using Android

The “Identification of Alphanumeric pattern using Android” is a smart phone apps using Android platform and combines the functionality of Optical Character Recognition and identification of alphanumeric pattern and after processing, data is stored in server. This paper present, to design an apps using the Android SDK that will enable the Identification of Alphanumeric pattern using optical character reader technique for the Android based smart phone application. Camera, captures the document image and then the OCR is convert that image in to text (Binarization of captured data) according to the Alphanumeric (alphabetic and numeric characters) database and data stored in server. DOI: 10.17762/ijritcc2321-8169.160414

HYBRID BINARIZTION TECHNIQUE FOR HISTORICAL MANUSCRIPTS

This paper presents a new hybrid approach for the binarization and enhancement of Historical Manuscript. This paper deals with degradations which occur due to shadows, non-uniform illumination, low contrast and strain. We follow two distinct method of Binarization with a pre-processing procedure using a adaptive Wiener filter, a rough estimation of foreground regions and a background surface calculation by interpolating neighboring background intensities. Further logical anding of the calculated background surface with compliment of second method result, performing final thresholding and post-processing in order to improve the quality of text regions. After extensive experiments, our method demonstrated superior performance against some wellknown techniques on numerous degraded document images as well as on Historical Manuscript in both manners qualitatively and quantitatively

Parking lot monitoring system using an autonomous quadrotor UAV

The main goal of this thesis is to develop a drone-based parking lot monitoring system using low-cost hardware and open-source software. Similar to wall-mounted surveillance cameras, a drone-based system can monitor parking lots without affecting the flow of traffic while also offering the mobility of patrol vehicles. The Parrot AR Drone 2.0 is the quadrotor drone used in this work due to its modularity and cost efficiency. Video and navigation data (including GPS) are communicated to a host computer using a Wi-Fi connection. The host computer analyzes navigation data using a custom flight control loop to determine control commands to be sent to the drone. A new license plate recognition pipeline is used to identify license plates of vehicles from video received from the drone

Model based methods for locating, enhancing and recognising low resolution objects in video

Visual perception is our most important sense which enables us to detect and recognise objects even in low detail video scenes. While humans are able to perform such object detection and recognition tasks reliably, most computer vision algorithms struggle with wide angle surveillance videos that make automatic processing difficult due to low resolution and poor detail objects. Additional problems arise from varying pose and lighting conditions as well as non-cooperative subjects. All these constraints pose problems for automatic scene interpretation of surveillance video, including object detection, tracking and object recognition.Therefore, the aim of this thesis is to detect, enhance and recognise objects by incorporating a priori information and by using model based approaches. Motivated by the increasing demand for automatic methods for object detection, enhancement and recognition in video surveillance, different aspects of the video processing task are investigated with a focus on human faces. In particular, the challenge of fully automatic face pose and shape estimation by fitting a deformable 3D generic face model under varying pose and lighting conditions is tackled. Principal Component Analysis (PCA) is utilised to build an appearance model that is then used within a particle filter based approach to fit the 3D face mask to the image. This recovers face pose and person-specific shape information simultaneously. Experiments demonstrate the use in different resolution and under varying pose and lighting conditions. Following that, a combined tracking and super resolution approach enhances the quality of poor detail video objects. A 3D object mask is subdivided such that every mask triangle is smaller than a pixel when projected into the image and then used for model based tracking. The mask subdivision then allows for super resolution of the object by combining several video frames. This approach achieves better results than traditional super resolution methods without the use of interpolation or deblurring.Lastly, object recognition is performed in two different ways. The first recognition method is applied to characters and used for license plate recognition. A novel character model is proposed to create different appearances which are then matched with the image of unknown characters for recognition. This allows for simultaneous character segmentation and recognition and high recognition rates are achieved for low resolution characters down to only five pixels in size. While this approach is only feasible for objects with a limited number of different appearances, like characters, the second recognition method is applicable to any object, including human faces. Therefore, a generic 3D face model is automatically fitted to an image of a human face and recognition is performed on a mask level rather than image level. This approach does not require an initial pose estimation nor the selection of feature points, the face alignment is provided implicitly by the mask fitting process

Automatic Nutritional Information Extraction from Photographic Images of Labels

Nos últimos anos o interesse das pessoas em melhorar a sua dieta tem crescido. Muitos fatores podem ser apontados para este crescimento, sendo um destes o aumento alarmante das doenças relacionados com a alimentação. Este conjunto de doenças tornou-se progressivamente as causas mais comuns de morte e incluem doenças cardiovasculares, obesidade, diabetes e cancro. Atualmente quase todos os produtos alimentares presentes no mercado incluem rótulos nutricionais, estes rótulos constituem qualquer informação presente no pacote de um produto que se refira aos valores dos seguintes nutrientes: energia, proteínas, hidratos de carbono, gorduras, fibra, sódio, vitaminas e minerais. Esta informação fornece uma visão mais detalhada da composição de um produto e ajuda os consumidores a fazer escolhas mais saudáveis. Enquanto os rótulos não tiverem um formato regulamentado, cada produto pode apresentar a informação nutricional de maneira diferente, levando a uma enorme variedade de rótulos nutricionais presentes no mercado. Isto combinado com a grande quantidade de informação apresentada e a dificuldade em interpretar os dados nutricionais sem o conhecimento necessário, faz com que a análise dos rótulos nutricionais não seja uma tarefa fácil para os consumidores. Uma das soluções para simplificar esta tarefa, apontada em grande parte dos estudos realizados sobre este tópico, é a apresentação de um sumário da informação nutricional como complemento à apresentação exclusiva dos nutrientes e respetivos valores.O principal objetivo deste projeto foi ir ao encontro desta necessidade e oferecer uma ferramenta que ajude na extração e interpretação destes valores, usando uma aplicação Android. Esta aplicação tenta extrair automaticamente a informação nutricional e apresenta-a de uma forma transversal, seguindo as novas regulamentações e com algumas ajudas de interpretação como valores relativos às doses diárias recomendados e esquemas simplificados. Há também a possibilidade de comparar dois produtos da mesma categoria, ajudando o utilizador a avaliar qual dos produtos é a opção mais saudável. Para atingir estes objetivos, é necessário realizar a conversão da imagem em texto para mais tarde ser processado. Para isso foi usado o motor de OCR desenvolvido pela Google, Tesseract.Muitos problemas foram encontrados ao longo do desenvolvimento do projeto, tais como a baixa precisão do OCR usado ou os problemas derivados da aquisição das imagens usando um dispositivo móvel. No entanto, depois de alguns algoritmos de pré e pós processamento, a precisão aumentou para 55%, 83% a mais do que sem qualquer pré-processamento. Além disso, a percentagem de imagens que retorna 0 correspondências diminuiu de 30% para 8%.In the past years people showed an increasing interest in improving their diet. Many factors can be pointed to this growth, being one of them the alarming explosion of diet related diseases. This group of diseases is progressively becoming the most common causes of death, including cardiovascular diseases, obesity, diabetes and cancer.Currently, almost all food products on the market contain nutrition labels, which is any information that appears on the product package referring to the values of the following nutrients: energy, proteins, carbohydrates, fats, dietary fiber, sodium, vitamins and minerals. This information provides a great insight of a product composition and helps the consumers to make healthier food choices.While the labels do not have a regulated or standard format, each product often presents the nutrition information differently, leading to a wide variety of nutrition labels present in the market.This, combined with the high amount of information displayed and the difficulty of interpreting the data without the necessary knowledge, makes the extraction of relevant data and analysis a hard task for consumers. One of the solutions to simplify this task suggested in many of the studies on this subject, is to present a summary of nutrition information as a complement to the nutrient-specific information.The main outcome of this project is to overcome this problem and offer the consumer a tool to help in the extraction and interpretation of these values, by offering to the consumer an Android application. This application tries to extract automatically the nutritional information of an image of a nutrition declaration and presents it in a single, cross-sectional shape, following the new regulations and with some additional aids, including relative values to the recommended daily doses and simplified schemes. In addition to this feature, it is also possible to compare between two products of the same category.In order to achieve these goals, it is necessary to convert the image into digital text to be processed later. To perform this conversion the application uses the OCR engine developed by Google, Tesseract.Many problems were found throughout the development of this project, such as the low accuracy of the OCR engine or the problems of acquiring the images using a mobile device. However, after some pre and post processing algorithms, the accuracy increased to 55%, 83% more than without any preprocessing. In addition, the percentage of images that returns 0 matches decreased from 30% to 8%