How to use ABBYY FineReader

So, we have FineReader installed on our computer. We turn on the scanner and digitize some multi-page document. Let's call it, conditionally, "Agreement".

Place the first page of the document on the scanner glass and close the lid. Let's launch FineReader program. Click the “Scan” button, or press the “Ctrl+K” combination. The "ABBYY FineReader Scanning" window opens. When digitizing ordinary text page typed in 11-12 point font, leave the settings in the default window and click the “View” button.

The scanner works and after a few seconds we see our page in the viewing window. Here we can change the size of the scan if necessary. And then click the "Scan" button.

FineReader begins the text recognition process and within a minute the page image opens in the program window. Right side The window is now divided into three sections. In the left section "Image" we can edit the image. You can read more about image editing in the lesson: Scanning a book. In the right section "Text" you can immediately make changes to the text - edit the content of the page even before saving it. This is very convenient when you need, for example, to quickly change dates, details, and last names in a document.

An icon of the recognized page appears in the left part of the “Pages” window:

If you don’t need to edit anything, replace the first page on the scanner glass with the second page and repeat the technology. Having adjusted the scan sizes once in the "ABBYY FineReader Scanning" window in the "Preview" mode for the first page, now immediately click the "Scan" button. The settings for the first page are saved, and subsequent pages are scanned without preview. So we scan all the pages of our document.

We’ve finished, and now, by clicking on the icons one by one, we open the pages, checking their correct sequence.

After that, in the left part of the “Pages” window, select all the icons with the button: “Edit – Select all” or with the keyboard shortcut: “Ctrl + A”. Then, in the drop-down list next to the “Save” button, select the command: “Save as PDF document":

Now click on the button itself and save the document with the name “Agreement.pdf” in the “Agreement” folder:

As a result, we get a multi-page text document pdf format - electronic version our document with the code name "Agreement".

So, we digitize text documents using FineReader.

By changing the scanning mode to “color” in the “ABBYY FineReader Scanning” window, we can also easily digitize color pictures and photographs.

And by asking in context menu, for example, the command: "Save as document Microsoft Word 2007" we will transform our project into a single multi-page editable Word document.

In general, the program is easy to understand, intuitive and has pop-up tips everywhere.

Hello. Today I will talk about how to use the Abbyy FineReader program to recognize text from an image that you may have received as a result of scanning. Your scanned text will be completely in a Microsoft Word document and this recognized text can be edited! Recognize text when help Abbyy Finereader can be useful for those who study and work with texts and translations. The program, unfortunately, is paid. I once had a chance to try one of free options similar programs, but very well scanned text is recognized simply terribly... And text recognition in Abbyy FineReader turns out to be very high quality! Now I will show you how to use the Abbyy FineReader program to quickly recognize text from an image.

ABBYY FineReader has trial version for 30 days with the ability to recognize up to 100 pages and save no more than 3 pages from a document. Those. During this time, you can see the capabilities of the program and make an informed decision - whether you need it, whether it is worth buying or not.

How to install Abbyy FineReader!

Before using Abbyy Finereader you need to install it. Let's look at the installation process of this program...

First, select the program language. Click "OK".

We accept the terms license agreement(If you wish, you can read the license agreement if you are interested in what it is about). Click “Next”.

Next, you must select the installation mode. At normal mode the program will not ask you and will install what is specified in the program by default, namely all components: the Abbyy Finereader program itself for text recognition, a component for programs Microsoft Office and a component for Windows Explorer (allowing you to quickly recognize images without opening the program separately). I advise you to note custom installation to configure it the way you need. Moreover, it won’t take even 15 minutes :) Below is the folder where the program will be installed. It is advisable to leave the default selection so that there are no problems later when using the program. Click “Next”.

Program components. This window will appear if you select the “Custom” installation type. Components are something like auxiliary applications for a program. The first component “Integration with Microsoft programs Office and Windows Explorer" This component will be displayed in the Microsoft Office menu and if you right-click on the image on your computer, there will be an item with this program. This is what your menu will look like in Microsoft Office after adding this component.

Here's what happens if you right-click on the image:

Those. A menu will appear in which you can do quick text recognition and send the results to Word, Excel or PDF.

The second component will allow you to recognize text from your computer screen. This means that you can take a screenshot and also recognize the text. If you do not want to install one of these components, or do not want to install both, then you need to click on the down arrow and select “This component will not be available.” Then the component will not be installed. I left both.

Next 4 points. The first means that information about how you use the Abbyy Finereader program will be transferred to the developer. I advise you not to check this item so that the program does not once again go online to send information about working with it. Moreover, you never know what other information will be sent :) The 2nd point creates a shortcut to the program on the desktop. The 3rd means that the program will start when you turn on the computer, and the 4th will check for program updates. I leave only the second one and leave a tick next to it. Closing everything Microsoft applications Office, because the installer requires it and click “Install”.

You need to wait a couple of minutes for the program to load and click “Next”.

That's it, installation is complete! Click “Finish”.

How can I recognize text from a scanned or any other image using Abbyy Finereader?

Let's look at how to use the program. For example, you have scanned text. Now, to recognize text in Abbyy FineReader, open the program. Click “Open”.

Select the image we need and click open.

When you open required document, Abbyy Finereader will begin to recognize the text. The larger the document, the longer recognition will take. Recognition of one page may take several seconds.

After the text is recognized, all you have to do is save the result in Microsoft document Word so you can then edit anything in it. To do this, click the “Save” button on top panel tools, then select which folder the Word document will be saved in and under what name.

If you have a scanner connected to your computer, then you can start scanning directly from the program, and after which the scanned document will be immediately recognized. To do this, click the “Scan” button on the top toolbar. Next steps will depend on the driver program for your printer. You only need to follow the instructions of the scanning wizard.

As you can see, everything is very simple and fast. Now you know how to use Abbyy FineReader to recognize text from images! I hope this information will help a lot of people :) Good luck!

Although advances issued artificial intelligence(AI) over the past 50 years have not brought “smart” machines one iota closer to the cognitive capabilities of humans; it would be unfair to completely deny successes in this direction. The most obvious and striking example is chess (not to mention more simple games). A computer cannot yet imitate our thinking, but it is quite capable of compensating for this gap with a large amount of specialized memory and search speed. Vladimir Kramnik described the game of the Deep Fritz program that defeated him in 2006 as “inhuman” in the sense that it often contradicted the established (human) rules of strategy and tactics.

And just over a year ago, another brainchild of IBM, which at one time laid the foundation for the triumphant chess victories of computers (the famous Deep Blue), called Watson, made a new breakthrough, defeating two champions of the popular American quiz Jeopardy by a wide margin. It is significant, however, that although Watson independently voiced the answers, the questions were still transferred to him in text form. This suggests that successes in many areas of AI application - speech and image recognition, machine translation - are quite modest, although this does not prevent us from using them in practice today. The greatest successes, perhaps, are demonstrated by optical character recognition systems (OCR, Optical Character Recognition), with which almost all PC users are probably familiar in one way or another. Moreover, Russian developments in this area occupy a worthy place in the world - I mean ABBYY FineReader.

A little history

The current version of ABBYY FineReader is number 11, i.e. the application has gone through quite a long development path, and even the history of this process is of some interest. Without pretending to be an exhaustive chronicle, I will give only the main milestones over the last decade, during which I more or less followed FineReader:

Naturally, during the same time, FineReader expanded support for document formats, improved built-in tools and interface, improved reconstruction of the structure of originals, etc. However, the highlighted points are directly related to OCR technologies and demonstrate well the spasmodic development process characteristic of complex knowledge-intensive systems when after the next “breakthrough” there follows a certain period of “quiet”, necessary for improving new algorithms. They represent the main value of any OCR program, and therefore to some extent detailed information users rarely hear about them. However, ABBYY kindly agreed to lift the veil of secrecy, and today we have the opportunity to look into the holy of holies of FineReader.

Basic principles

So, since OCR belongs to the field of AI, it is logical that developers strive to at least to some extent imitate the activity of our brain. Of course, the structure of our visual system is incredibly complex, but the basic “large-block” principles of its functioning have been sufficiently studied; usually there are three of them:

1. Integrity- an object is considered as a collection of its parts and (for visual images) spatial relationships between them. In turn, the parts receive interpretation only as part of the entire object. This principle helps to build and clarify hypotheses, quickly eliminating unlikely ones.
2. Purposefulness- since any interpretation of data pursues a specific goal, recognition is a process of putting forward hypotheses about an object and purposefully testing them. A system operating in accordance with this principle will not only be more economical computing power, but also less likely to make mistakes.
3. Adaptability- the system saves the information accumulated during operation and reuses it, i.e. it learns itself. This principle allows you to create and accumulate new knowledge and avoid re-decision the same tasks.

FineReader is the only OCR system in the world that operates in accordance with the principles described above at all stages of document processing. The corresponding technology is called IPA- according to the first letters of English terms. For example, according to the principle of integrity, a fragment of an image will be interpreted as a symbol only if it contains all the structural parts of similar objects, and those that are in certain relationships. This helps to replace the search of a large number of standards (in search of a more or less suitable one) with a targeted test of a reasonable number of hypotheses, relying on previously accumulated information about the possible outlines of a character in a recognized document.

However, IPA principles apply when analyzing not only fragments corresponding to (presumably) individual characters, but also the entire source image of the page. Most OCR systems are based on recognizing the hierarchical structure of a document, i.e. the page is divided into basic structural elements such as tables, images, blocks of text, which, in turn, are divided into other characteristic objects - cells, paragraphs - and so on , down to individual characters.

Such an analysis can be carried out in two main ways: top-down, i.e., from constituent elements to individual characters, or, conversely, bottom-up. One of them is most often used, but ABBYY has developed a special algorithm MDA(multilevel document analysis), which combines both. Briefly, it looks like this: the structure of the page is analyzed using a top-down method, and the reconstruction electronic document upon completion, recognition occurs from the bottom up, but at all levels there is an additional mechanism feedback. As a result, the likelihood of gross errors associated with incorrect recognition of high-level objects is sharply reduced.

ADRT

Historically, OCR systems have evolved from recognizing individual characters. This task is still the most important and most difficult; the most complex algorithms are associated with it. However, it soon became clear that higher-level information (for example, about the language of the document and the correct spelling of recognized words) could help in solving this problem - this is how contextual and dictionary checks appeared. Then, the desire to preserve formatting and recreate the physical structure (i.e., the relative positions of various objects) of a document led to the need for detailed analysis of an entire page. It is clear that this also significantly affects overall quality recognition, since it helps to correctly process multi-column layout, tables and other techniques of “non-linear” text arrangement.

Most modern OCR operates precisely at these three levels - characters, words, pages - practicing, as already mentioned, top-down or bottom-up approaches. However, ABBYY, in accordance with the principles of IPA, introduced one more level into FineReader - a total multi-page document. First of all, this was needed to correctly reproduce the logical structure, which is becoming increasingly more complex in modern documents. But there is also additional bonuses: increased accuracy and faster processing of repeating objects, more correct identification (and therefore recognition) of objects “flowing” from page to page.

This is exactly why it was developed ADRT(Adaptive Document Recognition Technology) - technology for document analysis and synthesis at the logical level. Ultimately, it helps make the result of FineReader work as similar as possible to the original. To do this, the image of the entire document is analyzed, and the recognized words are combined into groups (clusters) depending on the style, environment and location on the page. In this way, the program seems to see the “logic” of the document markup and can subsequently unify the design of the result.

Thanks to ADRT, FineReader, starting with version 9.0, has learned to detect, recognize and reproduce the following structural parts and document formatting elements:

• main text;
• headers and footers;
• page numbers;
• headers of the same level;
• table of contents;
• text inserts;
• captions for drawings;
• tables;
• footnotes;
• signature/seal zones;
• fonts and styles.

Recognition process

In accordance with the MDA algorithm, the recognition itself starts from top to bottom, from the page level. It is clear that the more wrong decisions are made in the early stages of this process, the more there will be in the subsequent ones. This is why recognition accuracy depends so much on the quality of the originals, but their pre-processing algorithms can also have a significant impact. Thus, as the popularity of color documents grew in FineReader, an adaptive binarization procedure appeared. AB). If you scan a document immediately in black and white mode, where there are watermarks or the text is located on a textured or color substrate, then “garbage” will invariably appear on the image, which will then be quite difficult to separate from the “useful” image (since the original information about him is already lost). That is why FineReader prefers to work with color or grayscale images, independently converting them into black and white (this process is called binarization). But that's not all. Since the colors of text and background can vary within a page and even within individual lines, AB identifies words with more or less the same characteristics and selects optimal binarization parameters for each from the point of view of recognition quality. This is precisely the adaptivity of the algorithm, which is therefore an example of the use of feedback in MDA. It is clear that the effectiveness of AB strongly depends on the design of the source documents - on the ABBYY test base, this algorithm provided an increase in recognition accuracy by 14.5%.

But the most interesting, of course, begins when the recognition process descends to the lowest levels. The so-called linear division procedure splits lines into words and words into individual letters; then, in accordance with the IPA principle, it forms a set of hypotheses (i.e. possible options what kind of symbol it is, what symbols the word is divided into, etc.) and, providing each with a probability estimate, transmits it to the input of the character recognition mechanism. The latter consists of a number of so-called classifiers, each of which also generates a number of hypotheses ranked by their expected degree of probability. The most important characteristic of any classifier is the average position of the correct hypothesis. It is clear that the higher it is, the less work for subsequent algorithms - for example, dictionary checking. But for sufficiently well-established classifiers, characteristics such as recognition accuracy based on the first three hypotheses or only the first one are most often assessed - i.e., roughly speaking, the ability to guess the correct answer in three or one attempt. ABBYY uses in its systems following types classifiers: raster, feature, feature differential, contour, structural and structural differential - which are grouped at two logical levels.

Operating principle RK, or raster classifier, is based on a pixel-by-pixel comparison of a character image with standards. The latter are formed as a result of averaging images from the training set and reduced to a certain standard form; Accordingly, the size, thickness of elements, and slope are also pre-normalized for the recognized image. This classifier is characterized by ease of implementation, speed of operation and resistance to image defects, but provides relatively low accuracy and that is why it is used at the first stage - to quickly generate a list of hypotheses.

Feature classifier ( PC), as its name suggests, is based on the presence of signs of a particular symbol in the image. If there are N such features in total, then each hypothesis can be represented by a point in N-dimensional space; Accordingly, the accuracy of the hypothesis will be assessed by the distance from it to the point corresponding to the standard (which is also developed on the training set). It is clear that the types and number of features largely determine the quality of recognition, so there are usually quite a lot of them. This classifier is also relatively fast and simple, but is not very robust to various image defects. In addition, the PC does not operate with the original image, but with a certain model, an abstraction, i.e. it does not take into account some information: say, the very fact of the presence of some important elements says nothing about their relative position. For this reason, the PC is used not instead of, but together with the RK.

Contour classifier ( QC) represents special case PC differs in that it analyzes the contours of the intended symbol, extracted from the original image. In general, its accuracy is lower than that of a full-fledged PC.

Feature differential classifier ( MPC) is also similar to PC, but is used solely to distinguish between similar objects such as "m" and "rn". Accordingly, it analyzes only those areas where differences are hidden, and it receives as input not only the original images, but also hypotheses formed at the early stages of recognition. The principle of its operation, however, is somewhat different from a PC. At the training stage, two “clouds” (groups of points) of possible values ​​for each of the two options are formed in N-dimensional space, then a hyperplane is constructed that separates the “clouds” from each other and is approximately equidistant from them. The recognition result depends on which half-space the point corresponding to the original image falls into.

MPC itself does not put forward hypotheses, but only refines existing ones (the list of which is generally sorted using the bubble method), so that a direct assessment of its effectiveness is not carried out, but indirectly it is equated to the characteristics of the entire first level of OCR recognition. However, it is clear that it depends on the correctness of the selected features and the representativeness of the sample of standards, ensuring which is a rather labor-intensive task.

Structural differential classifier ( KFOR) was originally used for processing handwritten texts. Its task is to distinguish between similar objects such as “C” and “G”. Thus, SDK is based on features characteristic of each pair of characters, its learning process is even more complex than that of MDC, and its operating speed is lower than that of all previous classifiers.

Structural classifier ( SK) is a source of pride for ABBYY; it was originally developed for recognizing so-called handwritten text, i.e. when a person writes in “printed” letters, but was later used for printing. It is used at the final stages of recognition and comes into effect quite rarely, namely, only when at least two hypotheses with sufficiently high probabilities reach it.

The qualitative characteristics of all classifiers are collected in the following table. They, however, only allow one to evaluate the effectiveness of the algorithms relative to each other, since they are not absolute, but are obtained based on the processing of a specific test sample. It may seem that at the last stages of recognition the struggle is literally for a fraction of a percent, but in fact, each classifier makes a significant contribution to increasing the recognition accuracy - for example, the SC reduces the number of errors by a noticeable 20%.

* evaluation of the entire first level of the ABBYY OCR algorithm
** estimate for the entire algorithm after adding the appropriate classifier

It is curious, however, that, despite the rather high accuracy, the recognition algorithm itself does not accept final decision. In accordance with the MDA principle, hypotheses are put forward at each logical level, and their number can grow exponentially. Accordingly, sequential testing of all hypotheses is unlikely to be effective, and therefore ABBYY OCR systems use the method of structuring hypotheses, i.e., assigning them to one or another model. There are a couple dozen of the latter, here are just a few of their types: dictionary word, non-dictionary word, Arabic numerals, Roman numerals, URL, regular expression- and each can include many specific models(for example, a word in one of the known languages, Latin, Cyrillic, etc.).

All final actions are carried out with hypotheses built using models. For example, contextual checking will determine the language of the document and immediately significantly reduce the likelihood of models using incorrect alphabets, and dictionary checking will compensate for errors in the event of uncertain recognition of certain characters: for example, the word “turn” is present in the dictionary English language- in contrast to “tum” (in any case, it is not among the popular ones). Although the priority of the dictionary is higher than that of any classifier, it is not necessarily the last resort, and in general does not stop further checks: firstly, as mentioned above, there is a model of a non-dictionary word, and secondly, the special organization of dictionaries allows with a high percentage probabilities to guess whether some unknown word can belong to a particular language. However, dictionary checking (and the completeness of dictionaries) has a significant impact on the recognition result, and in ABBYY’s own tests it reduces the number of errors by almost half.

Not only OCR

Printed documents are far from the only ones of interest from the point of view of their digitization and automatic processing. Quite often you have to work with forms, i.e. documents with predefined and fixed fields that are filled out manually, but relatively accurately (so-called hand-printed characters) - an example is various questionnaires. The technology for their processing has a separate name - ICR(intelligent character recognition) - and differs quite significantly from OCR. Yes, since in in this case Since the task is not to recreate the entire document, but to extract specific data from it, it breaks down into two main subtasks: finding the necessary fields and actually recognizing their contents.

This is a fairly specific area, and ABBYY offers a completely separate software product, ABBYY FlexiCapture, for it. It is intended for creating automated and semi-automated systems, involves customization for specific types of documents for which special templates are created, can intelligently find various fields on pages and verify data in them, etc. However, at the very core are character recognition algorithms similar to those , which are used in FineReader, and general scheme very similar:

However, important difference nevertheless, there is: the structural classifier is an obligatory participant in the process - this is due to the specifics of hand-printed symbols. In addition, ICR involves a large number of specific additional checks: For example, whether the character is crossed out, or whether the recognized characters actually form a date.

Translating text to digital format- a fairly common task for those who work with documents. The Abbyy Finereader program will help you save a lot of time by automatically translating inscriptions from raster images or “readers” into editable text.

In this article we will look at how to use Abbyy Finereader for text recognition.

How to recognize text from a picture using Abbyy Finereader

In order to recognize text on raster image, you just need to load it into the program, and Abbyy Finereader will automatically recognize the text. All you have to do is edit it, highlight what you need and save it in the required format or copy it into a text editor.

You can recognize text directly from a connected scanner.

Read more on our website.

How to create a PDF and FB2 document using Abbyy Finereader

Abbyy Finereader program allows you to convert images into universal PDF format and FB2 format for reading on e-books and tablets.

The process for creating such documents is similar.

1. In the main menu of the program, select the E-Book section and press FB2. Select the source document type—scan, document, or photo.

2. Find and open the required document. It will load into the program page by page (this may take some time).

3. When the recognition process is completed, the program will prompt you to select a format for saving. Select FB2. If necessary, go to “Options” and enter additional information(author, title, keywords, description).

After saving, you can remain in text editing mode and convert it to Word format or PDF.

Features of text editing in Abbyy Finereader

There are several options for text that Abbyy Finereader recognizes.

In the original document, save the pictures and footers so that they are transferred to the new document.

Analyze the document to know what errors and problems may arise during the conversion process.

Edit the page image. Options for cropping, photo correction, and changing resolution are available.

So we told you how to use Abbyy Finereader. It has quite broad capabilities for editing and converting texts. Let this program help you create any documents you need.

One of the most popular functionality for working with scanning and file processing various types- Fine Reader. Functional software product was developed by the Russian company ABBYY, it allows not only to recognize, but also to process documents (translate, change formats, etc.). Many users can only install it, but cannot immediately figure out how to use ABBYY FineReader. You can find answers to many questions in this article.

The program allows you to scan and recognize text - and more

To understand in detail what kind of program ABBYY FineReader 12 is, you need to consider in detail all its capabilities. The first and simplest function is to scan a document. There are two scanning options: with and without recognition. If you scan a printed sheet normally, you will receive the image you scanned in the specified folder on your computing device.

ATTENTION. The sheet must be placed evenly on the scanning part of the printer, along the contours indicated on the printer. Do not allow the source code to be twisted, this may lead to poor quality final scan.

You must decide for yourself why you need FineReader, since the utility has significant functionality, for example, you can independently choose what color you want the image to be in, it is possible to convert all photos to black and white. In black and white, recognition is faster and the quality of processing increases.

If you are interested in the text recognition function of ABBYY FineReader, you need to press a special button before scanning. In this case, there are several options for obtaining information. As standard, a recognized piece of sheet will be displayed on your screen, which you can copy or edit manually.

If you select other functions, you can immediately receive the file as a Word document or Excel table. Selecting functions is very simple, the menu is intuitive and easy to customize due to the fact that all the buttons you need are in front of your eyes.

IMPORTANT. Before you recognize text ABBYY FineReader, you need to accurately select the processing language. Despite the fact that the utility works completely automatically, it happens that low quality the source does not allow us to understand what kind of language was in the source. This greatly reduces the quality of the final results of the application.

Multiple operating modes

To fully understand how to use ABBYY FineReader 12, you need to try two modes of operation: “Careful” and “Quick recognition”. The second mode is suitable for high-quality images, and the first for low-quality files. The Thorough mode takes 3-5 times longer to process files.

The illustration shows the result of the program - text recognition from an image

What other functions are there?

Text recognition in ABBYY FineReader is not the only one useful feature. For greater user convenience, there is