Optical character recognition

About: Optical character recognition is a research topic. Over the lifetime, 7342 publications have been published within this topic receiving 158193 citations. The topic is also known as: OCR & optical character reader.

NEOCR: a configurable dataset for natural image text recognition

Abstract: Recently growing attention has been paid to recognizing text in natural images. Natural image text OCR is far more complex than OCR in scanned documents. Text in real world environments appears in arbitrary colors, font sizes and font types, often affected by perspective distortion, lighting effects, textures or occlusion. Currently there are no datasets publicly available which cover all aspects of natural image OCR. We propose a comprehensive well-annotated configurable dataset for optical character recognition in natural images for the evaluation and comparison of approaches tackling with natural image text OCR. Based on the rich annotations of the proposed NEOCR dataset new and more precise evaluations are now possible, which give more detailed information on where improvements are most required in natural image text OCR.

Retail terminal utilizing an imaging scanner for product attribute identification and consumer interactive querying

Abstract: The present invention provides a system, method and apparatus for identifying a product through reading of the product label by a retail terminal. The product/product label is scanned by an imager of a retail terminal. An attribute recognition program such as an optical character recognition (OCR) program is used on the scanned product label which generates text strings from alphanumeric label information and graphics maps/images from graphics/logos. Text strings and/or graphics data are then compared to various text strings and graphics data in a database or look-up table to return information relative to the scanned text string(s)/graphic(s). In one form, kiosks, incorporating an imager and the necessary hardware and software to scan a product label and process the scanned information in accordance with the present principles, may provide printouts of product information, instructions, order forms or the like for the scanned product. Additionally, standard queries or user-generated queries may be answered relative to the scanned product label. Data, stored either locally or at a remote site accessible via a network or the like, is correlated to a plurality of text strings/graphics that correspond to alphanumeric text/graphics on a plurality of product labels.

System and method for translating languages using portable display device

Abstract: A method and system for translating written text from a first (foreign) language to a second (native) language is provided. An image containing the text is first captured at the request of the user. Text zones are identified in the image and the zones are converted to text characters using optical character recognition. The text characters, which are in the first language, are translated to the second language. The translated text is then output to the user. The text may be converted to an image that can be displayed on a display or, alternatively, the text may be synthesized into speech that may be played over a speaker accessible to the user such as an earpiece. Data can be provided to the user as text, audio or text and audio combined.

Offline Handwritten Script Identification in Document Images

Abstract: Automatic handwritten script identification from document images facilitates many important applications such as sorting, transcription of multilingual documents and indexing of large collection of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate a texture as a tool for determining the script of handwritten document image, based on the observation that text has a distinct visual texture. Further, K nearest neighbour algorithm is used to classify 300 text blocks as well as 400 text lines into one of the three major Indian scripts: English, Devnagari and Urdu, based on 13 spatial spread features extracted using morphological filters. The proposed algorithm attains average classification accuracy as high as 99.2% for bi-script and 88.6% for tri-script separation at text line and text block level respectively with five fold cross validation test. General Terms Pattern Recognition, Document Image Analysis

Automated labeling in document images

Abstract: The National Library of Medicine (NLM) is developing an automated system to produce bibliographic records for its MEDLINER database. This system, named Medical Article Record System (MARS), employs document image analysis and understanding techniques and optical character recognition (OCR). This paper describes a key module in MARS called the Automated Labeling (AL) module, which labels all zones of interest (title, author, affiliation, and abstract) automatically. The AL algorithm is based on 120 rules that are derived from an analysis of journal page layouts and features extracted from OCR output. Experiments carried out on more than 11,000 articles in over 1,000 biomedical journals show the accuracy of this rule-based algorithm to exceed 96%.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.