There’s always been a need to remove passwords from protected Microsoft Word documents when you need to make a change. My most common occurrence is with Human Resource (HR) forms. HR staff will create the MS Word document, password protect it, and then upload it to our company intranet. However, when I need to fill the form out, the only way to do so is to print the form, sign my name, scan the signed & printed form, and them email it back to them. What I would like to do, is to just paste a scan of my signature onto the form, save as a PDF, and them email them the PDF saving printing and scanning time and paper (which would then need to be shredded).
Here’s a trick that I learned a long time ago.
First, install the new Java version on your FAST ESP server.
Second, there are 3 configuration changes to inform FAST ESP which version of Java to use. Unfortunately, the FAST ESP application does not fully utilize the standard “JAVA_HOME” environment variable, so there are two additional configuration files that must be edited.
I’ve used SubSonic 2.x for a while and I’ve blogged about how useful it was as a tool to aid my development projects. A few weeks ago, SubSonic 3.0 was released and I hadn’t spent much time reviewing the updates and changes to version 3.0 until this week.
At the present, I have several large projects using SubSonic 2.x and I have a new smaller project starting this week. So I decided to download and install SubSonic 3.0 and use the small project to get familiar with the updates prior to updating my other web sites from 2.x to 3.0.
A few months ago, I wrote a small article about extracting pages from a PDF document to create a new PDF document. This article will use the same library, iTextSharp, to merge pages from one PDF document to create a second PDF document.
For this utility, imagine having a PDF document with pages that are 8 1/2″ x 11″ and you want to combine 2 pages into one larger page. The resulting output document would be 17″ x 11″ and show two pages from the input document on one page on the output document.
FAST ESP is a robust enterprise search platform that I’ve worked with and administer for a couple of internal projects. One of our requests for the FAST contractors was asking how we could fix the entity extraction algorithm to create a more accurate extraction.
The platform uses a complex method to determine and extract companies, persons, and location names. However, sometimes their algorithm provides false positives.
Here’s a few examples of what we saw as company names using the default FAST ESP company names extractor:
- Program Support
- Personnel Management
- IT Support
- JSP (meaning Java Server Pages)
- Database Design
Here’s a few examples of some person names extracted from our documents using the default FAST ESP person names extractor.
- Lan, Wan (meaning local area network, wide area network)
- Columbus Day
- West Virginia
- Norton Antivirus