Using Context and Interactions to Verify User-Intended Network Requests

Client-side malware can attack users by tampering with applications or user interfaces to generate requests that users did not intend. We propose Verified Intention (VInt), which ensures a network request, as received by a service, is user-intended. VInt is based on "seeing what the user sees" (context). VInt screenshots the user interface as the user interacts with a security-sensitive form. There are two main components. First, VInt ensures output integrity and authenticity by validating the context, ensuring the user sees correctly rendered information. Second, VInt extracts user-intended inputs from the on-screen user-provided inputs, with the assumption that a human user checks what they entered. Using the user-intended inputs, VInt deems a request to be user-intended if the request is generated properly from the user-intended inputs while the user is shown the correct information. VInt is implemented using image analysis and Optical Character Recognition (OCR). Our evaluation shows that VInt is accurate and efficient

Character-based Automated Human Perception Quality Assessment In Document Images

Large degradations in document images impede their readability and deteriorate the performance of automated document processing systems. Document image quality (IQ) metrics have been defined through optical character recognition (OCR) accuracy. Such metrics, however, do not always correlate with human perception of IQ. When enhancing document images with the goal of improving readability, e.g., in historical documents where OCR performance is low and/or where it is necessary to preserve the original context, it is important to understand human perception of quality. The goal of this paper is to design a system that enables the learning and estimation of human perception of document IQ. Such a metric can be used to compare existing document enhancement methods and guide automated document enhancement. Moreover, the proposed methodology is designed as a general framework that can be applied in a wide range of applications. © 2012 IEEE

Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues

Recognizing scene text is a challenging problem, even more so than the recognition of scanned documents. This problem has gained significant attention from the computer vision community in recent years, and several methods based on energy minimization frameworks and deep learning approaches have been proposed. In this work, we focus on the energy minimization framework and propose a model that exploits both bottom-up and top-down cues for recognizing cropped words extracted from street images. The bottom-up cues are derived from individual character detections from an image. We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them. These interactions are top-down cues obtained from a lexicon-based prior, i.e., language statistics. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets, namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word, and show better performance than comparable methods. We perform a rigorous analysis of all the steps in our approach and analyze the results. We also show that state-of-the-art convolutional neural network features can be integrated in our framework to further improve the recognition performance

Validación automática de guías de accesibilidad e videojuegos

Trabajo de Fin de Grado en Desarrollo de Videojuegos, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2021/2022.Accessibility is a fundamental aspect of video games. Any player should be able to enjoy a game, independently of their physical ability. All around the world, governments and associations have implemented standards for game developers to make sure their video games can be enjoyed by everyone. One of the cases to keep in mind when developing games, and the one this project is based on, is the case of people with deficient color vision or nearsightedness. These users can have difficulties seeing text in-game when it is relatively small when compared to screen size or when its color does not have enough contrast with the background. It is commonplace for companies to have a considerable team of testers who manually check for legal accessibility requirements as well as ensuring the quality of the product. These checks, although simple, are time consuming, taking up valuable time that could be used for more complex checks that could only be made by humans. Moreover, tendency to create increasingly content-packed games creates less manageable workloads for manual checks. For this project, a tool has been developed that automates the text detection and calculates its size and relative luminance contrast with the background. This tool is used to verify that a series of accessibility criteria are being met.La accesibilidad es un aspecto fundamental de los videojueogos. Todos los jugadores, independientemente de sus capacidades fisicas, deben poder disfrutar de ellos. Alrededor del mundo, distintos gobiernos y asociaciones han creado estándares con los que medir la accesibilidad de los videojuegos para que puedan ser disfrutados por todos los públicos. Uno de estos casos que hay que tener en cuenta al desarrollar videojuegos, y en el cual se centra este trabajo, es el de las personas con discapacidad visual como el daltonismo o la miopía. Ellos pueden tener dificultad para ver bien el texto en un juego cuando este es muy pequeño en relación al tamaño de la pantalla o cuando no hay suficiente contraste de color entre la letra y el fondo. A dia de hoy es comun que las empresas cuenten con una extensa plantilla de testers que comprueba manualmente los requerimientos legales y la calidad del juego. Estas comprobaciones, aunque sencillas, resultan laboriosas y consumen un tiempo muy valioso que podría dedicarse a pruebas sólo realizables por humanos. Además, la tendencia a crear juegos que tienen cada vez más contenido hace que este sea un volumen de trabajo cada vez menos asumible por una fuerza de trabajo manual. Este trabajo consiste en el desarrollo una herramienta que permite automatizar el reconocimiento de texto para detectar su tamaño y su contraste con el fondo, para asegurarnos de que cumple una serie de criterios que aseguran su accesibilidad a varios públicos.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Accuracy Affecting Factors for Optical Handwritten Character Recognition

Optiline kirjatuvastus viitab tehnikale, mis konverteerib trükitud, kirjutatud või prinditud teksi masinkodeeritud tekstiks, võimaldades sellega paberdokumentide nagu passide, arvete, meditsiiniliste vormide või tšekkide automaatset töötlemist. Mustrituvastus, tehisintellekt ja arvuti nägemine on kõik teadusharud, mis võimaldavad optilist kirjatuvastust. Optilise kirjatuvastuse kasutus võimaldaks paljudel kasvavatel informatsiooni süsteemidel mugavat üleminekut paberformaadilt digitaalsele. Tänapäeval on optilisest kirjatuvastusest väljaskasvanud mitme sammuline protsess: segmenteerimine, andmete eeltöötlus, iseloomulike tunnuste tuletamine, klassifitseerimine, andmete järeltöötlus ja rakenduse spetsiifiline optimiseerimine. See lõputöö pakub välja tehnikaid, millega üleüldiselt tõsta optiliste kirjatuvastussüsteemide täpsust, näidates eeltöötluse, iseloomulike tunnuste tuletamise ja morfoloogilise töötluse mõju. Lisaks võrreldakse erinevate enimkasutatud klassifitseerijate tulemusi. Kasutades selles töös mainitud meetodeid saavutati täpsus üle 98% ja koguti märkimisväärselt suur andmebaas käsitsi kirjutatud jaapani keele hiragana tähestiku tähti.Optical character recognition (OCR) refers to a technique that converts images of typed, handwritten or printed text into machine-encoded text enabling automatic processing paper records such as passports, invoices, medical forms, receipts, etc. Pattern recognition, artificial intelligence and computer vision are all research fields that enable OCR. Using OCR on handwritten text could greatly benefit many of the emerging information systems by ensuring smooth transition from paper format to digital world. Nowadays, OCR has evolved into a multi-step process: segmentation, pre-processing, feature extraction, classification, post-processing and application-specific optimization. This thesis proposes techniques to improve the overall accuracy of the OCR systems by showing the affects of pre-processing, feature extraction and morphological processing. It also compares accuracies of different well-known and commonly used classifiers in the field. Using the proposed techniques an accuracy of over 98% was achieved. Also a dataset of handwritten Japanese Hiragana characters with a considerable variability was collected as a part of this thesis