OCR-IT Cloud-based OCR API was one of the first high-quality online OCR Optical Character Recognition) services on the market. It launched in 2009 and started to appear in various implementations by 2010. On of the first apps on Apple Store was FotoNote app, which to this day gets 5-star rating due to high OCR quality. Many other apps followed with unique and creative uses of OCR. OCR-IT offers a number of plans and resources to enable iOS developers to use the OCR-IT API in their own apps.
All currently available pricing plans are listed here: Pricing Plans
- Development Account – developers receive Free account and full access to API for entire development and testing lifecycle. Full access to resources is provided along with live testing environment. Sign up to Development & Testing plan to start the development.
- Production Account – once the app is ready to go live, a different subscription from Development & Testing plan is needed. Developer can choose any other plan available from OCR-IT plan selection page, depending on the estimated volume of images to be processed. Alternatively, a custom plan can be discussed and created if Developer finds that a different licensing model will be more beneficial.
API Technical Resources
There are three major sources of technical information for iOS developers:
- API Documentation – detailed technical documentation explaining every part of OCR service and its usage.
- OCR-IT Blog – a number of articles containing tips, tricks and best approaches to creating powerful and effective OCR-based apps.
- OCR-IT Support Team – technical experts with many years of OCR and image processing experience. OCR-IT staff can help answer theoretic and practical questions regarding image and text quality, use of API, application design and best practices and procedures. Support team can also review image samples and provide specific suggestions. Support Team can be contacted from Contact Us page.
All available code samples are listed on Code Samples page.
All code samples were contributed by OCR-IT users from practical implementations. Currently, OCR-IT does not provide iOS code sample.
Have code? Want to contribute? Be the first to contribute iOS code sample using OCR-IT API and receive fame and 10,000 Units credit to your subscription account. Contact firstname.lastname@example.org for code sample submission procedures.
OCR-IT provides two optional source code packages to speed-up app development:
- Complete FotoNote source code. This is complete source code for a well-designed and completely runable OCR app listed currently in App Store. This app contains entire process for taking pictures, processing images with OCR-IT API, and storing and exporting results. Complete source code can be purchased and repurposed or modified in whole or in part to help developers cut down on their development cycle. App can be tested by downloading it from App Store here. Source code is provided as compileable project with as-is warranty. Source code includes a powerful Binarization Module (see below). Cost of purchase at the time of this writing is $1495.00. Contact email@example.com for current costs and terms and conditions.
- FotoNote Binarization Module source code. This code segment is an important part of FotoNote source code, but it can be purchased separately since every app can use its benefits. Binarization is a process of converting color images to black-and-white layer, which then can be used for processing. Binarization is a powerful and important step in OCR process, which provides multiple benefits, and without which apps work slower and produce less quality than with it. See this article and this article for further explanation of binarization.
- Benefits of on-device binarization:
– Clear on-screen image preview for quality. It is much easier and faster to review and see a low quality image if it is presented in black-and-white instead of color.
– Up to 30x faster upload time. Original image from camera is about 2 MB. Image after binarization is about 70 KB. Image has tiny size without sacrificing OCR text quality.
– Faster back-end processing. Once submitted to cloud-based processing, images do not need to be binarized on backend, and smaller images have some processing priority.
– Higher text quality. OCR-IT backend binarization is tuned for mixed documents (office papers, camera images, video frames, screenshots, etc.). This binarization is fine-tuned for iPhone and iPad images.
– Cleaner image readability. Binarization removes shadows, noise and gradient colors to reveal simple high contrast black-and-white image.For example, this original image from iPhone 4S: 2.99 MBresults in the following black-and-white image for OCR submission: 0.9 MBCost of purchase at the time of this writing is $695.00. Contact firstname.lastname@example.org for current costs and terms and conditions.
Custom App Development
OCR-IT team works with many iOS developers, who in turn have demonstrated practical understanding and implementation of OCR on mobile devices, and successfully utilized OCR-IT API in their Apps. If you have an App idea and would like to find a professional App developer instead of writing the App code yourself, or modifying or FotoNote project, we can connect you to additional resources. Please contact our Support Team at email@example.com with a brief description of your project and a request to be connected to an App developer, and we’ll be glad to assist you.
If you require any additional information about OCR-IT API, assistance with any material listed above, or have other questions in general, please contact our Support Team.
Ilya Evdokimov is a long-term practitioner and expert in leading Optical Character Recognition (OCR), Data Capture and Document Processing techniques, technologies and solutions. With over 15 years of experience spanning enterprise software implementations, mobile applications development, cloud-based systems integration and desktop-level automation, Ilya Evdokimov uses through industry knowledge and experience to achieve high efficiency and workflow optimization in most challenging paper-dependent and digital image capture environments.