Advanced Development Tools

Advanced developer tools in ABBYY FineReader Engine 12: predefined and custom profiles, multi-CPU recognition, and the Project Tool for testing scenarios.

Useful tools that enhance the developer’s ability to interact with ABBYY FineReader Engine and manipulate the recognition process on the core level:

Working with Profiles

ABBYY FineReader Engine 12 provides a set of predefined profiles which are already fine-tuned for the basic usage scenarios. The settings specified in these profiles provide the best results in the corresponding situations. Besides, most of the profiles come in two forms: with the settings optimized for the best quality of the resulting document or with the settings optimized for the highest speed of processing. Below is a list of available predefined profiles:

Scenario	Profile Name
Data extraction	DataExtraction
Document conversion for content reuse	DocumentConversion_Accuracy DocumentConversion_Normal
Document archiving	DocumentArchiving_Accuracy DocumentArchiving_Speed
Text extraction for fields detection and documents classification	TextExtraction_Accuracy TextExtraction_Speed
Field-level recognition	FieldLevelRecognition
Barcode recognition	BarcodeRecognition_Accuracy BarcodeRecognition_Speed
Business cards recognition	BusinessCardsProcessing
Data capture from a machine-readable zone	MachineReadableZone
Document archiving in high-compressed PDF	HighCompressedImageOnlyPdf
Technical drawings recognition	EngineeringDrawingsProcessing

You can view the list of settings provided by these profiles in Predefined Profiles Specification.

The settings provided with these profiles can be loaded using the LoadPredefinedProfile method of the Engine object. After the profile is loaded, newly created objects will have the new default values specified in the profile.

Voting API support

When ABBYY FineReader Engine is used as one of the participating recognition engines in a third-party application, it supplies recognition alternatives (or hypotheses) with a relevant confidence level for characters, words and intercharacter separation. This information helps developers design an efficient and accurate voting algorithm for applications that require multiple recognition technologies.

For example, when recognizing an “O”, ABBYY FineReader Engine may return 3 hypotheses: “0” (zero), with confidence 60; capital “O”, with confidence 80; and capital “C”, with confidence 10. In the case of intercharacter separation, the situation can be like this: the possible hypotheses for an “m” would be “m”, “rn”, and “in”. See more in Using Voting API.

”On-the-fly” tuning of core recognition

ABBYY FineReader provides developers with the access and ability to manipulate the recognition engine during the OCR process on a core level. The FineReader recognition engine generates hypotheses (or recognition alternatives) and allows developers to influence or fine-tune the procedure of setting the confidence level for each hypothesis (or selecting the best hypothesis) using their own specific ranking criteria.

Code Samples for common conversion tasks

The SDK is supplied with the set of Source Code Samples showing how to use the Engine in different scenarios. Windows Code Samples are provided for Visual Basic .NET, raw C++, C++ with the Native COM Support, C#, Java and scripting languages.

See also

Key Features