ABBYY FineReader Engine ABBYY FineReader Engine

The most comprehensive OCR SDK for software developers

Integrate AI-powered OCR features into your applications.

Recognition: comprehensive set of recognition technologies

For the actual text recognition step, ABBYY FineReader Engine offers a comprehensive set of recognition technologies. The provided technologies include recognition of machine-printed texts (OCR), hand-printed texts (ICR), recognition of optical marks (OMR) and recognition of barcodes (OBR). As a market leader, ABBYY offers the highest number of OCR languages, which can be individually combined. See below the list of available technologies and processing options.

Optical Character Recognition (OCR)

OCR technology is available for more than 200 languages:

  • European languages (Latin, Cyrillic, Armenian, Greek alphabets) 
  • Non-European languages: Chinese, Japanese, Korean, Arabic, Farsi, Thai, Vietnamese, Hebrew, Burmese (preview)
  • FineReader XIX – for old documents, books and newspapers published from 1600 until 1937 in English, French, German, Italian and Spanish in old fonts such as Fraktur, Schwabacher and Gothic fonts
  • Recognition of OCR-A, OCR-B, MICR (E13B) and CMC7 fonts and documents printed by dot-matrix printers or typed on typewriters 

Intelligent Character Recognition (ICR)

ICR technology is available for more than 120 languages:

  • European and non-European languages
  • 22 regional styles of hand-printing
  • Recognition of hand-printed characters in fields and frames
  • ICR for Indian digits used in Arab states

Recognition of hand-printed information in different languages (multilingual ICR) is possible.  

Optical Mark Recognition (OMR)

The ABBYY’s OMR technology recognizes simple checkmarks, grouped checkmarks, model checkmarks and checkmarks with “corrections” made by hand in different variations:

  • Checkmarks in square frame
  • Checkmarks against the empty background
  • Non-standard checkmark types (requires training)

Optical Barcode Recognition (OBR)

  • 1D and 2D barcode types
  • Fast barcode extraction. This feature enables automated detection and recognition of barcodes at any angle on a document.

Recognition modes

By defining a recognition mode, developers can quickly set up and tune the processing speed and accuracy. In addition to the default-processing mode, both OCR and ICR recognition can be performed in normal, fast and balanced recognition modes:

  • Normal recognition mode

    The most accurate mode for achieving the highest quality of recognition, for example if you are planning to reuse the recognized content.

  • Fast recognition mode

    This mode increases the processing speed by 200-250%, making the technology ideal for content management (CMS), document management (DMS) and archiving systems.

  • Balanced recognition mode

    This mode provides higher speed than the Normal mode, while achieving almost the same accuracy.

Full text recognition and field-level recognition

In general, two types of recognition are possible: full text and field-level recognition. Full text recognition is used for document conversion and usually includes usage of OCR technology. Field-level recognition is used to extract particular data and includes usage of OCR, ICR and other technologies.

The following table shows the differences:

Specification Full text recognition Field-level recognition
Used for: Document conversion, books archiving Data capture / Data extraction
Document analysis: General document analysis, document analysis for invoices, document analysis for full-text indexing Manual blocks specification for field-level recognition
Recognition technologies: OCR with up to 99% accuracy OCR, ICR, OMR, Barcodes recognition with predefined data types and values range. 99.99% accurate
Verification: Recommended (for content reuse) Obligatory (as accuracy is a critical issue in most cases)
Synthesis Used for document reconstruction Not used
Export format Document files (RTF, DOC, PDF, etc.) Export to XML file or database

Full text recognition

Full text recognition is a basic recognition type for different tasks, like:

  • Documents and books conversion for archiving
  • Document conversion for content reuse 
  • Ground text extraction for fields detection and documents classification

All of them require the recognition (OCR) of the whole text. Resulting text is exported as a plain text or as a complete document in the requested format.

Field-level recognition

To support key business processes such as forms processing, keyword classification, machine vision or robotic process automation, ABBYY FineReader Engine extracts text from fields or zones. Key functionality includes multilingual OCR and ICR, OMR, barcode recognition and a range of specific functions, such as:

  • Data extraction from fields with various borders and frames
  • Definition of field content by setting alphabets, dictionaries, regular expressions, handwriting styles, etc. 
  • Detection of in-field spacing
  • Intelligent processing of blocks with intersecting parts and lines
  • Text block despeckling, with the ability to specify the size of "garbage"

Field-level recognition is as well supported by special tools for developers such as Voting API and "On-the-Fly" Recognition Tuning. 

User languages

To increase the recognition quality, ABBYY FineReader Engine provides an API for creating and editing recognition languages, adjusting predefined recognition languages, and adding new words to user languages. Examples:

  • To improve the quality of ICR recognition in forms, you can use user languages to describe the type of information, which may be entered in each field (zip codes, product codes, numbers).
  • To improve the recognition of product codes, telephone numbers or passport numbers, you can create a new recognition language, which helps the program to read specific types of data.

Pattern training

In the vast majority of cases, FineReader Engine can successfully read texts without any prior training. However, when recognizing decorative or outlined fonts or low print quality documents, own patterns can be trained and the recognition quality increased.

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