Low Resolution OCR for Portable Devices to Assist the Visually Impaired

适用于便携式设备的低分辨率 OCR 为视障人士提供帮助

• 批准号: 7480848
• 负责人: Ari Gross
• 金额: $ 10万
• 依托单位: CVISION TECHNOLOGIES, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2009-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7480848
• 关键词: Address; Algorithms; Back; Blur; Cellular Phone; Computer software; Computers; Contracts; Development; Devices; Equipment; Generic Drugs; Grant; Image; Imaging Device; Internet; Legal; Letters; Life; Lighting; Methods; Personal Digital Assistant; Phase; Printing; Public Health; Reading; Research; Resolution; Sampling; Scanning; Source; Speech; System; Technology; Telephone; Text; Travel; Visual impairment; Visually Impaired Persons; Work; day; digital; improved; innovation; novel; optical character recognition; size; software development

DESCRIPTION (provided by applicant): CVISION Technologies Inc. proposes the development of novel optical character recognition (OCR) algorithms for increasing the independence of visually impaired people and improving their day-to-day life. The project will result in OCR software that will allow for using digital cameras, including cell phone cameras, to read documents, street signs, and other text. The text can then be read back to the user using text-to-speech technology, or sent to a computer for display or printing at a legible size. In either mode, the software will grant the visually impaired user greater independence while, for example, traveling or signing legal forms, without the need for specialized equipment or the assistance of other people. The research will specifically address the problem of accurate OCR for text in low resolution media such as digital video or digital camera images. The low resolution text in images from these devices gives current OCR technology a great deal of difficulty. The central innovation in this work is the development of a bottom-up approach to OCR without first thresholding the image into black & white (i.e. text and background). This is in contrast to conventional OCR systems, which depend upon the existence of a threshold to separate the text from the background, and then apply a top-down approach. The top-down approach breaks down the text in the image into increasingly smaller units (paragraph, line, word etc.), down to the glyph (character) level. This works well when the sampling resolution is topology-preserving, usually higher than about 200 dpi. The proposed bottom-up approach works in the opposite fashion, identifying the smallest units of the text first and building up to larger and larger units. The vague borders between blurred, undersampled letters that are typical of low resolution captured text can be effectively handled by relaxing the need for thresholding the grayscale image followed by the bottom-up approach, whereas they cause conventional OCR algorithms to fail. Another benefit of this method is that it does not require a calibrated scanning system (e.g. uniform lighting, horizontal text) to operate successfully. This will further increase the accuracy of the CVISION system for text in images captured by cell phone and other non-calibrated sources. PUBLIC HEALTH RELEVANCE: The proposed project's relevance to public health is its value to empower the visually impaired to live their lives with a greater deal of independence. For example, imagine being able to take a photograph of an apartment leasing contract that is illegible to a visually impaired person with an ordinary cell phone camera, using the software developed in this project to perform OCR on the image of the contract, and then having the cell phone read the contract with the phone's speaker.

描述(由申请人提供)：CVISION Technologies Inc.提议开发新型光学字符识别(OCR)算法，以提高视障人士的独立性，并改善他们的日常生活。该项目将产生OCR软件，该软件将允许使用数码相机，包括手机相机，来阅读文档、街道标志和其他文本。然后，可以使用文本到语音转换技术将文本读回给用户，或者将文本发送到计算机以便以清晰的大小显示或打印。在任何一种模式下，该软件都将在旅行或签署法律表格时给予视障用户更大的独立性，而不需要专门的设备或其他人的帮助。这项研究将专门解决在低分辨率媒体(如数字视频或数码相机图像)中文本的准确OCR问题。这些设备图像中的低分辨率文本给当前的OCR技术带来了很大的困难。这项工作的中心创新是开发了一种自下而上的OCR方法，而不需要首先将图像阈值化为黑白(即文本和背景)。这与传统的OCR系统不同，传统的OCR系统依赖于阈值的存在来将文本与背景分开，然后应用自上而下的方法。自上而下的方法将图像中的文本分解为越来越小的单位(段落、行、单词等)，一直到字形(字符)级别。当采样分辨率保持拓扑结构(通常高于约200 dpi)时，这种方法效果很好。提议的自下而上的方法以相反的方式工作，首先确定文本的最小单位，然后逐步扩大到更大的单位。作为低分辨率捕获文本的典型特征的模糊、欠采样字母之间的模糊边界可以通过放松对灰度级图像进行阈值处理的需要然后采用自下而上方法来有效地处理，而这会导致传统的OCR算法失败。该方法的另一个优点是，它不需要校准的扫描系统(例如，均匀照明、水平文本)即可成功操作。这将进一步提高CVISION系统对手机和其他未校准来源捕获的图像中文本的准确性。公共卫生相关性：拟议的项目与公共健康的相关性在于它的价值在于使视障人士能够更独立地生活。例如，想象一下，能够用普通手机摄像头拍摄一份对视障人士来说难以辨认的公寓租赁合同的照片，使用该项目中开发的软件对合同图像进行OCR，然后让手机通过手机的扬声器阅读合同。