SCANNED TEXT WORD RECOGNITION METHOD AND APPARATUS

• US 20140133767A1
• Filed: 11/08/2013
• Published: 05/15/2014
• Est. Priority Date: 11/09/2012
• Status: Abandoned Application

• Current Assignee: Brigham Young University (The Church of Jesus Christ of Latter-Day Saints)
• Original Assignee: Brigham Young University (The Church of Jesus Christ of Latter-Day Saints)
• Inventors: Lund, William B., Ringger, Eric K.
• Application Number:
  US14/075,187
• Publication Number:
  US 20140133767A1
• Time in Patent Office: Days
• US Class Current: 382/229
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/162 Quantising the image signal
• CPC Class Codes: G06V 30/268 Lexical context

Abstract

A method for converting digital images to words includes receiving a digital image comprising text, generating a binary image from the digital image for each of N binarization threshold values to provide N binary images, converting each of the N binary images to text, and aligning the text from the N binary images to provide a word lattice for the digital image. Aligning the text may include prioritizing the text from the N binary images according to error rates on a training set. The training set may be a synthetic training set. An apparatus corresponding to the above method is also disclosed herein.

Claims

1. A method for converting digital images to words, the method comprising:

receiving a digital image comprising text;

generating a binary image from the digital image for each of N binarization threshold values to provide N binary images, where N is greater than or equal to 2;

converting each of the N binary images to text; and

aligning the text from the N binary images to provide a word lattice for the digital image.

2. The method of claim 1, wherein aligning the text comprises prioritizing the text from the N binary images according to error rates on a training set.

3. The method of claim 1, wherein the training set is a synthetic training set.

4. The method of claim 1, further comprising inserting gaps within the text of a higher priority binary image to facilitate alignment.

5. The method of claim 1, wherein the N binarization threshold values are equally spaced.

6. The method of claim 1, further comprising selecting a word transcription from among alternative transcription hypotheses encoded in the word lattice using a selection model.

7. The method of claim 1, wherein the selection model leverages a textual context.

8. The method of claim 1, further comprising enabling a user to select a word sequence from the word lattice to provide a selected word sequence.

9. The method of claim 1, further comprising initiating an action corresponding to text within the word lattice.

10. An apparatus for converting digital images to words, the apparatus comprising:

a processor for executing one or more modules;

a binarization module configured to receive a digital image comprising text and generate a binary image from the digital image for each of N binarization threshold values to provide N binary images, where N is greater than or equal to 2;

an OCR module configured to convert each of the N binary images to text; and

an alignment module configured to align the text from the N binary images to provide a word lattice for the digital image.

11. The apparatus of claim 10, wherein the alignment module prioritizes text from the N binary images according to error rates on a training set.

12. The method of claim 11, wherein the training set is a synthetic training set.

13. The apparatus of claim 10, wherein the alignment module is further configured to insert gaps within the text of a higher priority binary image to facilitate alignment.

14. The apparatus of claim 10, wherein the N binarization threshold values are equally spaced.

15. The apparatus of claim 10, further comprising a transcription module configured to select a word transcription from among alternative transcription hypotheses encoded in the word lattice using a selection model.

16. The apparatus of claim 10, wherein the selection model leverages a textual context.

17. The apparatus of claim 10, further comprising a user interface module configured to enable a user to select a word sequence from the word lattice to provide a selected word sequence.

18. The apparatus of claim 10, further comprising a command module configured to initiate an action corresponding to text within the word lattice.

19. A computer readable medium comprising executable instructions for converting digital images to words, wherein the executable instructions comprise the operations of:

receiving a digital image comprising text;

generating a binary image from the digital image for each of N binarization threshold values to provide N binary images, where N is greater than or equal to 2;

converting each of the N binary images to text; and

aligning the text from the N binary images to provide a word lattice for the digital image.

20. The computer readable medium of claim 19, wherein the instructions further comprise the operation of selecting a word transcription from among alternative transcription hypotheses encoded in the word lattice.