Abstract:
A system 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&#39;ed and repeatedly verified until they meet a predetermined quality criteria. Characters or words 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 keyed-in. Those symbols that are manually classified go to OCR training and then to the known type optimization process.

Description:
RELATED APPLICATIONS 
   The present application is a continuation of prior U.S. application Ser. No. 12/040,946, filed Mar. 3, 2008, the entirety of which is incorporated herein by reference. 

   FIELD OF THE INVENTION 
   This invention relates to Optical Character Recognition (OCR). It specifically relates to a system of adaptive OCR for books and other documents with known and unknown characters or fonts. 
   BACKGROUND OF THE INVENTION 
   OCR (Optical Character Recognition) has become one of the most widely used tools of modern document processing. Typical commercial OCR engines are designed for the recognition of a wide variety of text images ranging from letters and business forms to scientific papers. It is common wisdom that superior performance can be achieved if OCR engine is trained to each specific type of documents. Hence, adaptive OCR engines have been developed. In these engines, automatic recognition results are corrected and the OCR engine is being adapted “on the fly.” Large digitization efforts are done today on library collections and archive centers around the world. These efforts scan books, newspapers and other documents, OCR them and create an electronic representation of the content. Hence, the importance of OCR quality is growing. Unfortunately, commercial OCR engines are imperfect. Some improvement can be achieved by performing spelling check using language dictionaries. However, such dictionaries tend to be incomplete (especially for historic texts and/or texts containing many special terms/names). Improvements due to these adaptive approaches remain insufficient. Hence, library collections and archive centers must either tolerate low quality data or invest large amounts of money in the manual correction of the OCR results. 
   SUMMARY OF THE INVENTION 
   It is an aspect of an embodiment of the present invention to provide an adaptive optical character recognition (OCR) system for a book having at least one page with known and unknown fonts. The system comprises the entire book, wherein the entire book is scanned and OCR of the entire book is performed. A verification process is applied for the known fonts, wherein the unknown fonts are clustered by shape. At least one sure word is separated out from at least one error words, wherein the sure words and error words are sent to an OCR training. Outliers are determined and the unknown fonts are manually classified. The manually classified fonts are sent to OCR training and the manually classified fonts are processed through the verification for known fonts and the sure words are separated out from error words. Manually classified results are processed to a quality evaluator. 
   The sure words are placed in a depository if they meet a predetermined quality criteria and sure words, error words and manually classified fonts are processed as required using an iterative process until all fonts meet the predetermined quality criteria. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is the high level system architecture according to an embodiment of the present invention. 
       FIG. 2  is a known fonts optimization process according to an embodiment of the present invention. 
       FIG. 3  is an unknown fonts optimization process according to an embodiment of the present invention. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   Throughout the specification the terms “font” or “font type” refer to the font shape or font size. For example, the letter “k” here is a Times New Roman font shape with a font size 12. Both the font shape and font size can be handled separately by the present invention. A high level system architecture  100  according to an embodiment of the present invention is depicted in  FIG. 1 . An entire book is scanned and processed (including image enhancement, de-noising and de-skewing) at  101 . It is noted that according to an embodiment of the present invention, the book at  101  may be scanned at any resolution. Preferably, the present invention uses 300 dpi. However, the system/process is designed to handle poor image quality scans down to 100 dpi. The conventional OCR is applied (e.g. using one of the commercial omni-font OCR engines) at  102 . This process creates the first draft of the recognition results. In addition, book text is split in the uniform areas corresponding to different languages (in multi-lingual books) and different fonts that may be used for each of the languages at  103 . Each such font type would be handled separately. For fonts that are known to the system at  104 , the system progresses to a known fonts optimization  105  to be discussed in reference to  FIG. 2 . For those font that are not known to the system at  104 , the system progresses to  106  to an unknown fonts optimization process  106  to be discussed in reference to  FIG. 3 . It is noted that those skilled in the art will realize that the method of  FIG. 1  may be modified without departing from the spirit and scope of the present invention. 
   According to one embodiment of the present invention instead of adapting one all purpose OCR as in the conventional OCR processing, the present invention uses a bank of adaptive OCR engines each tuned to the specific font type. For example, the present invention may include a Times New Roman OCR engine and a font size 12 OCR engine. In principle, the present invention distinguishes between two possible scenarios:
         1) When given font is known to the system   2) When given form is completely unknown.
 
In the first case, the present invention assumes that the omni-font OCR provides reasonable (though imperfect) results. Hence, draft OCR results can be used for further OCR tuning or training. In the second case, symbols to be recognized may be totally unknown to the system. Therefore, some draft OCR capabilities must be created before optimization can be started.
       

   A known fonts optimization process  200  according to an embodiment of the present invention is depicted in  FIG. 2 . Draft OCR results  201 - 203  are fed into the verification system  204 . Verification system  204  may use dictionaries of the given language or dictionaries compiled for the given domain (i.e. the subject domain relevant to the given book). The present invention can apply statistical tables (such as probabilities of occurrences of different character strings as computed for the entire book). Such verification processes would identify recognition results deemed to be sure (e.g. cases where a fuzzy search has identified a given word in the dictionary and there are no other likely candidates). Each “sure word”  206  determines some correct characters (i.e. characters such that OCR results match the recognized word). In addition, some OCR errors  205  may be found as well for cases where OCR result doesn&#39;t match the word in question. This information (regarding correct and incorrect recognition results) can be used in order to train the OCR engine at  207  (for instance, if OCR is based on the template matching approach, character images can be used in order to learn appropriate character templates valid to the specific book in question). Retrained OCR at  207  can be used for repeated recognition at  208  followed by another verification at  209 . Thus quality of the OCR results would be improved in an iterative manner. After each iteration, image quality would be estimated at  210 - 211 . Results surpassing customer requirements/predetermined quality criteria (e.g. high confidence OCR results that match dictionary/statistical tables expectations) would be passed to the final output repository at  212 . Remaining results would be routed by scheduler  213  to manual verification/correction at  214 - 215 . However, in this embodiment proposed by present invention, manual verification process would be controlled by the adaptive process requirements. Accordingly, text to be corrected would be sent to the scheduler module  213 . There, recognition results would be analyzed and adaptive needs would be determined. For instance, if text contains many low confidence characters “a” that means that the “a” template must be adjusted. Accordingly, OCR adaptivity requires a large number of “a” samples. In order to obtain such samples, the system would extract large number of character images likely to be “a”. These images are sent for manual verification creating high quality data to be used for OCR training purposes. It is noted that according to one embodiment of the present invention, it is not necessary to process the book or document sequentially. Conventional OCR engines process page 1, then page 2 and so on. According to an embodiment of the present invention, certain characters on page 10 may need to be processed first and then the method may return to page 1 or another page of particular interest based on character content. 
   In addition to character level training, one method according to the present invention calls for simultaneous adaptation of the verification dictionaries. Consider for example an historic book dealing with the First World War. The names of the politicians are likely to be excluded from the general purpose dictionaries. However, they can be identified as strings reoccurring in the text. Of course, such reoccurring strings can be also caused by the OCR errors. Accordingly, manual word verification at  215  would be used in order to determine whether a given string should be added to the specific book dictionary at  216  or discarded. The entire process would be repeated as required until the entire text passes the predetermined quality criteria. It is noted that those skilled in the art will realize that the method of  FIG. 2  may be modified without departing from the spirit and scope of the present invention. 
   Naturally, the aforementioned approach according to an embodiment of the present invention makes sense only if the initial draft results have some level of accuracy. Otherwise, no automatic verification and adaptation are possible. Now consider, for example, the case of a book or document containing unknown symbols (e.g. company logos). Clearly, no standard OCR will work for such symbols. Accordingly, the present invention proposes through another embodiment of a system/process for unknown fonts optimization as depicted in  FIG. 3 .  FIG. 3  illustrates an unknown fonts optimization process  300  according to an embodiment of the present invention. There, the system  300  will segment the text image at  301  in order to identify individual symbol images (e.g. by assuming that each connected component is a symbol). Then symbols are be clustered by shape at  302 . Each cluster is presumed to contain different samples of the same symbol. Classification of the symbol can be done manually at  304  (e.g. by showing one representative image for manual key-in by a skilled operator). Once the classification is known at  305 , the OCR engine can be trained at  306 . Finally, the optimization process  200  of  FIG. 2  at  307  is applied in order to optimize and refine recognition results. It is noted that those skilled in the art will realize that the method of  FIG. 3  may be modified without departing from the spirit and scope of the present invention. 
   In reference to  FIGS. 1-3 , it is noted that the system/method according to the embodiments of the present invention OCR&#39;s the entire book or document all at once and then makes an analysis that determines the optimal path for the most effective error detection. 
   Those of ordinary skill in the art will recognize that the steps shown in  FIGS. 1-3  can be performed in any order and are not limited to the embodiments described herein. 
   The invention has been described in detail with particular reference to certain preferred embodiments thereof, but it will be understood that variations and modifications can be effected within the spirit and scope of the invention.