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- Newsgroups: comp.ai.neural-nets
- Path: sparky!uunet!cs.utexas.edu!qt.cs.utexas.edu!yale.edu!jvnc.net!nuscc!iti.gov.sg!news
- From: kaykit@iti.gov.sg (Chan Kay Kit)
- Subject: Information on OCR products needed
- Message-ID: <1992Aug31.053123.16931@iti.gov.sg>
- Sender: news@iti.gov.sg (News Admin)
- Reply-To: kaykit@iti.gov.sg
- Organization: Information Technology Institute, National Computer Board, S'pore
- References: <BtM3GA.1zC@news.cso.uiuc.edu>
- Date: Mon, 31 Aug 1992 05:31:23 GMT
- Lines: 54
-
- Hello everyone,
-
- There are many commercial OCR engines on the market right now for
- reading printed text. I am aware of products like the Kurzweil K-5000 and
- Discover 7320 Model 5 (from Xerox), OmniPage (from Caere Corp.), SAIC (from
- Differential Processing System) and NestorReader.
-
- I wonder if anybody has done independent evaluation of any OCR
- products, since such information would be useful to potential users.
- Specifically, I need answers to the following questions:
-
-
- (i) name of product and company which produces it
-
- (ii) fonts and point sizes handled by product
-
- (iii) whether the OCR engine exhibits true multifont and "multisize"
- capability i.e. is able to recognize text without the user
- having to specify in advance its font type and point size.
-
- If so, what is the accuracy over a variety of fonts and point sizes?
-
- If not, what is the accuracy over common fonts like Courier, Times
- Roman and Helvetica and point sizes from 8 to 24 points?
-
- (iv) What are the commercial OCR products, if any, that can
- 1. recognise a page of text containing a mixture of fonts and point
- sizes at greater than 98% accuracy?
-
- 2. recognise a page of text containing a single known font and a
- range of point sizes at greater than 99% accuracy?
-
- (v) Are there any commercial OCR systems that accept noisy input or
- text on shaded background eg. carbon and photostated/fax copies and
- still maintain an acceptable accuracy (say greater than 95%)?
-
-
- Further, if there are any evaluations of commercial handwritten OCR systems,
- I will also be interested to hear about them.
-
- Please email all replies/references to kaykit@iti.gov.sg. If there are
- enough responses, I will summarise.
-
- Thanks!
-
-
-
-
- ---
- Kay-Kit CHAN | Internet: kaykit@iti.gov.sg
- Knowledge Systems Lab | Bitnet: kaykit@itivax
- Information Technology Institute | Tel: (65) 772-0920
- National Computer Board of Singapore | Fax: (65) 770-3043
-
-