Disclosed embodiments of the invention provide automated global optimization methods and systems of OCR, tailored to each document being digitized. A document-specific database is created from an OCR scan of a document of interest, which contains an exhaustive listing of words in the document. Images of each word, taken from all the fonts encountered, are entered into the database and mapped to a corresponding textual representation. After entry of a first instance of an image of a word written in a particular font, each new occurrence of the word in that font can be quickly recognized by image processing techniques. The disclosed methods and systems may be used in conjunction with adaptive character recognition training and word recognition training of the OCR engines.

OCR of books by word recognition

A system/method is presented for scanning entire books or document all at once using an adaptive process where the book or document has known fonts and unknown fonts. The known fonts are processed through a verification system where sure words and error words are determined. Both the sure words and error words are sent to OCR training where they are re-OCR'ed and repeatedly verified until they meet a predetermined quality criteria. Characters or word not meeting the predetermined quality criteria receive additional OCR training until all the characters and words pass the predetermined quality criteria. Unknown fonts are scanned and clustered together by shape. Outliers in the shapes are manually key-in. Those symbols that are manually classified go to OCR training and then to the known type optimization process.

Adaptive OCR for books

A character image is inputted by use of a scanner, and recognized. The resultant character string of such recognition is represented on a display. The image serving as recognition source of the character designated on the display screen thereof, and the image in the vicinity of such image are represented. A character frame, which can discriminate the character image serving as recognition source, is edited in order to designate a new character image. This image and the inputted character information are registered on a character recognition dictionary correspondingly. Thereafter, the character recognition is carried out even with the utilization of such newly registered character. As a result, the recognition rate of the character recognition increases one after another.

An apparatus for recognising characters and a method therefor

Gerät und Verfahren zur Zeichenerkennung

Intelligent optical character reader (IOCR) for reading printed English text with alphanumeric symbols is presented. There are seven major functional units in the system: a self trainer, a graphic editor, a database manager, a token extractor, a pattern matcher, a spelling checker and a guess by feature analyzer. The first three units provides facilities for reference pattern manipulation, while the last four units are for character recognition. The spelling check and the feature analysis are based on some intelligent statistical rules, and together they enhance the recognition rate. >

K.H. Lee

Papers

IOCR: an intelligent optical character reader