OCR-IT Web OCR API Free Account Setup & Testing in Fiddler Video

OCR-IT, provider on high-accuracy Web-based OCR API for platform-independent development of text recognition from images, presents… How to integrate and test OCR API in two minutes (video) [su_youtube_advanced url=”http://youtu.be/pLblxSgg4yU” rel=”no” theme=”light”] OCR-IT API will provide you with powerful capabilities to convert images to highly accurate text data.  It is currently used in numerous mobile applications, desktop software, and enterprise solutions.  Now you can use it, too. First, open a free OCR API development account by visiting http://www.ocr-it.com/free-ocr-cloud-2-0-api-trial. Click on a big red “SIGN UP NOW” button to open a new account, or LOGIN link to access your existing account.  Once the account is open, you are ready to start you development and testing. Next, write your code or use your testing environment to create an OCR-IT API request via a Web call.  For this testing, we will use Fiddler, a free Web monitoring utility. Your request will consist of only three easy parts: Request URL  This is a special URL to which you will submit your OCR requests.  It contains your Secret Key.  Both the submit URL and you Secret key can be found in your OCR-IT account. Header Make sure you are creating a POST request, and your header contains the appropriate header information. Request Body  The body should contain XML with your request specifics.  It can be very minimal as only the image URL being submitted for OCR is required.  All other settings will be used as default.  Or it may contain other settings in case you prefer to overwrite any default values, such as OCR language or other parameters. This XML is provided in the API...

OCR-IT Web OCR API Free Account Setup & Testing in Fiddler Video

OCR-IT, provider on high-accuracy Web-based OCR API for platform-independent development of text recognition from images, presents… How to integrate and test OCR API in two minutes (video) OCR-IT API will provide you with powerful capabilities to convert images to highly accurate text data.  It is currently used in numerous mobile applications, desktop software, and enterprise solutions.  Now you can use it, too. First, open a free OCR API development account by visiting http://www.ocr-it.com/free-ocr-cloud-2-0-api-trial. Click on a big red “SIGN UP NOW” button to open a new account, or LOGIN link to access your existing account.  Once the account is open, you are ready to start you development and testing.   Next, write your code or use your testing environment to create an OCR-IT API request via a Web call.  For this testing, we will use Fiddler, a free Web monitoring utility. Your request will consist of only three easy parts: Request URL  This is a special URL to which you will submit your OCR requests.  It contains your Secret Key.  Both the submit URL and you Secret key can be found in your OCR-IT account. Header Make sure you are creating a POST request, and your header contains the appropriate header information. Request Body  The body should contain XML with your request specifics.  It can be very minimal as only the image URL being submitted for OCR is required.  All other settings will be used as default.  Or it may contain other settings in case you prefer to overwrite any default values, such as OCR language or other parameters. This XML is provided in the API Documentation, accessible through...
Recognizing 7-segment LCD display characters using OCR-IT API

Recognizing 7-segment LCD display characters using OCR-IT API

This question has been asked by a developer on Stackoverflow (source link).  OCR-IT API came to the rescue. “I’m trying to develop a Windows Phone 8.1 App but I need to recognize some numbers from different Displays.” I wish the answer to your question would be “Sure, here it is” with a link to a black-box process-anything OCR tool, but there are several stages and steps to the success are involved, which are best considered separately. First, there is some work on image pre-processing BEFORE you even consider any OCR. These image samples are very drastically different, and include full range of issues. SAMPLE 1 has low contrast, so when it is binarized to black and white layer, which most OCR will perform internally at some stage, there are no characters to process. It looks like this after binarization: See this OCR Blog post for additional details on image pre-processing: http://www.ocr-it.com/guide-to-better-mobile-images-from-cell-phone-camera-for-higher-quality-ocr. Secondly, the image has no dpi information in the header, which some OCR technologies use to determine appropriate scaling of the image. Without header information, some OCR programs may set some default dpi, which may or may not match your image, thus affecting the OCR result. This is NOT critical, but preferred if this can be implemented at the time of picture creation. SAMPLE 2 has sufficient contrast and adaptive notarization returns a clear image. It is also missing dpi resolution value in the header. SAMPLE 3 has very clear contrast, but it also has no resolution dpi in the header. Once you have images that are optimized for OCR processing, the next step is to look at OCR technologies. In...

Recognizing 7-segment LCD display characters using OCR-IT API

This question has been asked by a developer on Stackoverflow (source link).  OCR-IT API came to the rescue. “I’m trying to develop a Windows Phone 8.1 App but I need to recognize some numbers from different Displays.” I wish the answer to your question would be “Sure, here it is” with a link to a black-box process-anything OCR tool, but there are several stages and steps to the success are involved, which are best considered separately. First, there is some work on image pre-processing BEFORE you even consider any OCR. These image samples are very drastically different, and include full range of issues. SAMPLE 1 has low contrast, so when it is binarized to black and white layer, which most OCR will perform internally at some stage, there are no characters to process. It looks like this after binarization: See this OCR Blog post for additional details on image pre-processing: http://www.ocr-it.com/guide-to-better-mobile-images-from-cell-phone-camera-for-higher-quality-ocr. Secondly, the image has no dpi information in the header, which some OCR technologies use to determine appropriate scaling of the image. Without header information, some OCR programs may set some default dpi, which may or may not match your image, thus affecting the OCR result. This is NOT critical, but preferred if this can be implemented at the time of picture creation. SAMPLE 2 has sufficient contrast and adaptive notarization returns a clear image. It is also missing dpi resolution value in the header. SAMPLE 3 has very clear contrast, but it also has no resolution dpi in the header. Once you have images that are optimized for OCR processing, the next step is to look at OCR technologies. In...

Helping in research to extract OCR data from WWII records

Someone asked: “I am working on a research project that deals with American military casualties during WWII. Specifically, I am attempting to construct a count of casualties for each service at the county level. There are two sources of data here, each presenting their own challenges. 1. Army and Air Force data.  2. Navy and Marine Core data.” Full question is here: http://datascience.stackexchange.com/questions/5047/ocr-text-recognition-and-recovery-problem/5078#5078 Source 1 Sample Image | Source 2 Sample Image The answer to both data sets is an OCR application with some post-processing, but a more specialized program than a generic low-quality or an open source OCR. Essentially the harder the problem, the more capable and advanced tools need to be used to solve it. There will be two major stages in this task: generating the data (image to text, i.e. OCR), and processing the data (doing the actual count). Look at them separately in order to select the best method for each stage. The main challenges in these images and OCR are: a) images have low resolution. For example the # 1 image has resolution of about 72 dpi. Suggested resolution for such text quality is to scan at 300 to 400 dpi, but it is clear that re-scanning or controlling scan resolution is not applicable now. That’s why one option is to clean and increase the size using image pre-processing tools. This is what the original #1 image snippet looks like after adaptive binarization and zoomed at 300%. It is clear that each character has too few pixels and characters can be easily misread.   b) GIF format in #1 is not supported by many OCR...