Development of Optical Character Recognition Software Package for Mobile Phones

Optical Character Recognition (OCR) is a technique through which any textual information contained in images are extracted and converted into editable text format. The various OCR software packages which are available in desktop computer with scanner suffer from one primary constraint- MOBILITY. We have developed an OCR application for mobile phones. All the procedures needed for extracting the text would be performed within the mobile phone, eliminating the need for bulky devices like scanners, desktops and also laptops. Hence it would provide the user the much needed ‘anywhere, anytime’ feature for OCR. The computational power of mobiles is increasing day by day making it easier to run image processing operations for OCR application. Also the resolution of camera in mobile is increasing to match the resolution of scanners. After the document is processed, it can be communicated to another user by email facility of mobile phones as text files. The aim of this paper is to investigate the various issues involved in developing this OCR application in mobile phones. Further design and future scope for this application is elaborated giving insight to the development process. The motivation here was to provide a general purpose framework for OCR application in mobile phones. The framework is developed in a modular fashion

A Client mobile application for Chinese-Spanish statistical machine translation

This show and tell paper describes a client mobile application for Chinese-Spanish machine translation. The system combines a standard server-based statistical machine translation (SMT) system, which requires online operation, with different input modalities including text, optical character recognition (OCR) and automatic speech recognition (ASR). It also includes an index-based search engine for supporting off-line translation.Postprint (published version

Mobile text reader for people with low vision

People with low vision have visual acuity less than 6/18 and at least 3/60 in the better eye, with correction. The limited vision requires them to enhance their reading ability using magnifying glass or electronic screen magnifier. However, people with severe low vision have difficulty and suffer fatigue from using such assistive tool. This paper presents the development of a mobile text reader dedicated for people with low vision. The mobile text reader is developed as a mobile application that allows user to capture an image of texts and then translate the texts into audio format. One main contribution of this work compared to typical optical character recognition (OCR) engines or text-to-speech engines is the addition of image stitching feature. The image stitching feature can produce one single image from multiple poorly aligned images, and is integrated into the process of image acquisition. Either single or composite image is subsequently uploaded to a cloud-based OCR engine for robust character recognition. Eventually, a text-to-speech (TTS) synthesizer reproduces the word recognized in a natural-sounding speech. The whole series of computation is implemented as a mobile application to be run from a smartphone, allowing the visual impaired to access text information independently

Optical Character Recognition (OCR) for Mobile Application

The usefulness of integrating different techniques in wireless applications has brought the needs for providing better services in different technical sectors. Wireless Application Protocol (WAP) has been widely used for obtaining the required connection between clients via their handheld devices. This study highlights the difficulties that are faced by travelers in understanding foreign text during their journeys to other countries with different native languages. Hence, this study aimed to provide a solution by developing a mobile application based optical character recognition (OCR) for extracting the textual elements from the images. Asprise used in this study to extract the image text contents, meanwhile, Google API translation also used to translate the extracted contents into the selected language. The experiment result indicated that using Asprise OCR in extracting the text elements from the image was high accuracy among the free and simple OCR

Image processing for the extraction of nutritional information from food labels

Current techniques for tracking nutritional data require undesirable amounts of either time or man-power. People must choose between tediously recording and updating dietary information or depending on unreliable crowd-sourced or costly maintained databases. Our project looks to overcome these pitfalls by providing a programming interface for image analysis that will read and report the information present on a nutrition label directly. Our solution involves a C++ library that combines image pre-processing, optical character recognition, and post-processing techniques to pull the relevant information from an image of a nutrition label. We apply an understanding of a nutrition label\u27s content and data organization to approach the accuracy of traditional data-entry methods. Our system currently provides around 80% accuracy for most label images, and we will continue to work to improve our accuracy