Fast key-in for machine-printed OCR-based systems

• US 8,103,132 B2
• Filed: 03/31/2008
• Issued: 01/24/2012
• Est. Priority Date: 03/31/2008
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Tzadok, Asaf, Walach, Eugeniusz
• Primary Examiner(s): Bella, Matthew
• Assistant Examiner(s): Rush, Eric
• Application Number:
  US12/060,150
• Publication Number:
  US 20090245659A1
• Time in Patent Office: 1,394 Days
• Field of Search: 382/100, 382/161, 382/182, 382/198, 382/200, 382/209, 382/213, 382/215, 382/219, 382/220, 382/309, 382/310, 382/311
• US Class Current: 382/311
• CPC Class Codes: G06V 10/987 with the intervention of an operator

Abstract

A method for correcting results of OCR or other scanned symbols. Initially scanning and performing OCR classification on a document. Clustering character/symbol classifications resulting from the OCR based on shapes. Creating super-symbols based on at least a first difference in the shapes of the clustered characters/symbols exceeding a first threshold. A carpet of super-symbols, emphasizing localized differences in similar symbols, is displayed for analysis testing. Depending on results of analysis testing, performing one of: (1) storing the clustered symbols when the carpet of super-symbols passes all of the analysis testing; (2) creating additional super-symbols based on at least a second difference in the shapes of the clustered symbols exceeding a second threshold and returning to analysis testing when the carpet of super-symbols passes most of the analysis testing; and (3) rejecting the clustered symbols when the carpet of super-symbols fails most of the analysis testing and manually keying-in the symbols.

Claims

1. A method for correcting results of optical character recognition (OCR) comprising:

scanning a document image;

performing OCR classification on the document image;

clustering symbols from the OCR classification based on shapes of the symbols;

creating super symbols based on first differences in the shapes of the clustered symbols exceeding a first threshold and that provides a display that emphasizes localized differences in similar symbols;

displaying a carpet of super symbols for analysis testing;

and performing one of;

storing the clustered symbols when the carpet of super symbols passes all of the analysis testing;

creating additional sub groups of super symbols based on second differences in the shapes of the clustered symbols exceeding a second threshold and returning to the displaying step when the carpet of super symbols passes most of the analysis testing; and

rejecting the clustered symbols and storing the rejected clustered symbols by manually keying-in the symbols when the carpet of super symbols fails most of the analysis testing.