OCR Ranking 2025 – Comparison of the Best Text Recognition and Document Structure Software 

OCR (Optical Character Recognition) technologies have come a long way – from simple character reading in document scans to advanced systems recognizing the structure of invoices, forms or tables. In the era of business process automation, however, it is not only the recognition of text that is crucial, but also understanding the context and layout of the document. In our practical test, we compared 8 leading OCR tools in terms of their effectiveness in processing real business documents. Here are the results. 

The New Face of OCR – Intelligent Processing of Business Documents 

Text recognition based on document and image scans is no longer just an addition to archiving systems – in many organizations it is the foundation of process automation, such as accounting, logistics, HR or compliance with regulations. Modern OCR tools are no longer just text extraction engines, but complex AI systems that can recognize the structure of documents, identify data fields, segment tables or understand the context of information contained in forms. 

In corporate environments, OCR is increasingly expected not only to “read” documents, but also to understand them – e.g. distinguishing the invoice header from the goods item line, extracting the payment date, NIP number, gross amount or recipient data. What’s more, these documents come in various formats – from editable PDFs, through scanned images, to photos sent from mobile devices. In addition, they are often bilingual – e.g. Polish-English invoices for foreign partners. 

That is why more and more attention is being paid to testing OCR tools in the context of real applications – not only in terms of precision of letter recognition, but also in terms of the quality of document layout mapping, structural consistency, data completeness and processing flexibility. 

In our test, we focused on exactly this – comparing the effectiveness of different OCR engines in recognizing the structure of real structured documents. We tested 8 popular tools – both commercial cloud solutions and open-source frameworks that can be implemented locally or modified to suit your needs. All of them passed the same set of tests based on the analysis of a 10-page document package containing invoices, forms, declarations, customs documents and numeric-text tables – in Polish and English, in various input formats. 

Later in the article, we will show which tools coped best with data mapping, what difficulties they had, and which of them offer the best possibilities within several categories. If you are faced with choosing OCR technology for your project, this ranking will provide you with hard data, not marketing declarations. 

How We Tested Text Recognition Software? OCR Tool Comparison Methodology 

Choosing the best OCR tool cannot be based solely on manufacturer declarations or function descriptions. In order to reliably compare the capabilities of available solutions, it was necessary to design a test that reflects real business scenarios in which text recognition technologies and document structures are most often used. 

Purpose of OCR Test 

Our focus was not only on the precision of text recognition, but above all on the ability of tools to interpret the layout of a structured document – to recognize data fields, tabular structures, header sections and logical relationships between document elements. 

In practice, this meant analyzing the results against the following metrics: 

• data completeness (whether all relevant information was recognized) 
• field extraction correctness (whether, for example, “Issue Date” was correctly extracted as a separate value) 
• hierarchy and segment mapping (e.g., separation of headers from product lines) 
• table and column segmentation correctness 
• identification of numeric versus text data 
• correctness when recognizing bilingual documents 

OCR Test Set 

All tools passed the same test – they processed the same set of documents prepared from real examples encountered in everyday business work. The tests included both PDF files (editable and scanned) and images, and the results were sent in JSON, XLSX or TXT format – depending on the output format of the tool. The test package contained 10 pages in Polish and English, representing the following types of documents: 

• VAT and proforma invoices 
• forms and declarations 
• customs and logistics documents 
• transfer confirmations 
• tables of goods items and numerical data 

Results and Analysis of Results 

The text recognition results were saved by us in native formats for a given tool: JSON, XLSX or TXT. The format was not assessed, as long as it contained data that could be structurally compared. The key element of the assessment was the ability to map: 

• field labels and values  
• tabular items 
• logical relations (e.g. which value applies to which header) 
• compliance with reference data 

For the comparison, we used semi-automatic analytical tools and manual validation, especially where it was required to analyze the meaning of data and the relations between data (e.g. whether “Issue Date” was not recognized as “Sale Date”). Each tool was evaluated using uniform test data, and the scoring was standardized to easily compare different approaches: commercial APIs, open-source, AI/ML models, classic OCR, layout parsers, etc. 

The scoring description for the comparison columns is as follows: 

Category	What was Assessed	Scoring Criteria (0–10)
OCR/Text Extraction Accuracy	How well the tool recognizes text (letters, numbers, symbols) in various documents.	10-9 – error-free OCR, even in difficult layouts (small font, tables)  8-7 – a few minor typos  6-4 – frequent errors in numbers and names  3-1 – text practically illegible or incorrect
Document Structure Recognition	Does the tool detect document sections (e.g. headers, signatures, addresses), column layout, multi-page structure.	10-9 – full structure, divided into sections and their types  8-7 – correct physical structure (layout), no semantics  6-4 – text items only  3-1 – no division at all
Table Extraction Quality	How well tables are recognized: columns, rows, headers, numeric values.	10-9 – full tables with preserved structure and column names  8-7 – tables with minor errors (spilled columns, missing rows)  6-4 – data only as a text string  3-1 – no tables detected
Performance and Processing Time	How fast the tool processes documents and whether it scales for larger collections.	10-9 – processing < 3 sec/document, stable API or local  8-7 – average time (5–10s), stable performance  6-4 – delays / batch limitations  3-1 – processing in minutes or unstable
Ease of Integration and Use	Does the tool have a well-documented API, friendly output, SDK, GUI or does it work locally.	10-9 – ready API/SDK with documentation, convenient formats (JSON/XLSX)  8-7 – light customization needed  6-4 – difficult integration, poor documentation  3-1 – no documentation or manual configuration
Structured Document Support	How well does it handle typical documents: invoices, transfers, notifications, forms.	10-9 – automatic field detection, invoice semantics, address data, etc.  8-7 – well-represented structure, but no classification  6-4 – layout only, no context  3-1 – no support for technical documents

 

Standardization of Test Conditions 

 We ran all tools in the most similar conditions possible: 

• production versions 
• without additional training or learning 
• identical source files 
• one processing session – no manual corrections or interventions 

We conducted the test in April 2025, all tools were up-to-date at the time of the test. In the case of cloud solutions, we used the official API in production versions. 

OCR Test – 8 Text Recognition and Document Structure Software 

#1 Azure Form Recognizer 

General Characteristics 

The Azure Form Recognizer model is based on document layout detection – it does not use specialized semantic recognition for invoices or forms, but offers solid text and structure recognition (lines, words, page layout). 

Azure Form Recognizer Evaluation – Test on 10 Documents 

Category	Score (max 10)	Comment
OCR/Text Extraction Accuracy	10/10	Very high quality OCR – text recognized almost error-free, even for more difficult layouts (e.g. small font, technical fragments).
Document Structure Recognition	6/10	Document layout reproduced well (lines, words, items), but no automatic classification of sections (e.g. headings, form fields, invoice).
Table Extraction Quality	4/10	No native table analysis – data available as linear text, without row and column structure.
Performance and Processing Time	9/10	Fast processing, results available in seconds – ideal for production applications with high document volumes.
Ease of Integration and Use	8/10	Clear JSON data format, good API and documentation. However, it requires an additional layer of interpretation (e.g. field mapping).
Structured Document Support	6/10	It works well for technical documents and invoices, but without semantic analysis of structures (e.g. no information about the meaning of fields, no tables).

Conclusion 

Azure Form Recognizer in layout mode is an enterprise-class tool that works great as a very accurate OCR with document layout mapping. However, it does not provide ready-made semantic interpretation (like Google or ABBYY) and does not recognize tables as data structures. It is an excellent base for your own processing, but requires an additional layer of analysis (e.g. rules, NLP or ML).  

Final score: 7.2 / 10 

#2 Amazon Textract 

General Characteristics 

Amazon Textract is an OCR service from AWS that automatically recognizes text and data from documents, including tables and forms. It is AI-based, allows you to analyze documents in various formats (PDF, JPG, PNG), and easily integrates with other AWS services. Ideal for document processing automation. 

Amazon Textract Evaluation – Test on 10 Documents 

Category	Score (max 10)	Comment
OCR/Text Extraction Accuracy	8/10	Most of the text fragments were correctly recognized with high “Confidence” values.
Document Structure Recognition	7/10	Hierarchical output (BLOCK, LINE) segments the document well, although sometimes requires further mapping to specific fields.
Table Extraction Quality	8/10	Amazon Textract is good at detecting tables and their structure (rows, columns, headers) — even from scanned files. However, tables are not perfectly standardized — they sometimes require adjustment and interpretation, especially with a complex layout.
Performance and Processing Time	9/10	The service works quickly, which is typical for cloud solutions, and the result is returned in a short time.
Ease of Integration and Use	8/10	Amazon Textract offers a well-documented API; the JSON output is detailed, although it may require additional processing.
Structured Document Support	8/10	In the case of invoices and similar documents, the tool correctly extracts key data (invoice numbers, addresses, contact details).

Conclusion 

Amazon Textract performed very well in analyzing the scanned invoice. The system correctly extracted most of the data, and a high level of “Confidence” indicates high quality of recognition. The structure of the document was rendered in the form of a hierarchical division into blocks and lines, which allows for further processing, although it requires some refinement in mapping to specific fields. Overall, the solution is solid, fast, and well suited for integration with systems processing structured documents. 

Final score: 8 / 10 

#3 Google Document AI 

General Characteristics 

Google Document AI is a cloud service based on artificial intelligence that automatically analyzes and extracts data from text documents and scans. It offers advanced OCR with recognition of structures such as tables, form fields and semantic document layouts. It supports various document types (invoices, contracts, identity documents) and integrates with other Google Cloud services. Particularly effective in processing business documents and automating workflows. 

Google Document AI Evaluation – Test on 10 Documents 

Category	Score (max 10)	Comment
OCR/Text Extraction Accuracy	8/10	The full text of the invoice was extracted correctly, with high confidence values. A few minor distortions can still be corrected.
Document Structure Recognition	7/10	Google Document AI returns both flowing text and layout data (bounding boxes, segmentation), but segmenting individual fields requires additional processing.
Table Extraction Quality	8/10	Google Document AI effectively detects tables, headers, and rows, returning detailed layout and positioning data. However, it lacks full semantic classification of cells (e.g., “price”, “quantity”) and is not always ideal for more complex or unusual tables (e.g., merged columns, scattered values).
Performance and Processing Time	9/10	The service works very quickly, which is typical for cloud solutions – documents are processed in a short time.
Ease of Integration and Use	8/10	The API is well documented and the responses (in JSON format) contain detailed structured data, although mapping to your own models may require additional steps.
Structured Document Support	8/10	Key invoice data (addresses, numbers, item table, product codes) were correctly extracted, confirming the tool’s suitability for documents with a complex structure.

Conclusion 

Google Document AI results are very satisfying – the system effectively extracts both text and document structure elements, which allows for further automation of invoice processing. Despite minor imperfections in field segmentation, the exported data (along with information on location and recognition certainty) provide a solid basis for further analysis and integration with business systems. 

Final score: 8 / 10 

#4 Adobe PDF Extract API 

General Characteristics 

Adobe PDF Extract API is a cloud service based on Adobe Sensei technology, enabling accurate extraction of text, structures (tables, headings, paragraphs) and graphic elements from PDF documents. It is distinguished by high precision of document layout mapping and preservation of semantic context (e.g. heading hierarchy). Ideal for processing professional documents, reports, presentations – wherever structure is important, not just raw text. 

Adobe PDF Extract API Evaluation – Test on 10 Documents 

Category	Score (max 10)	Comment
OCR/Text Extraction Accuracy	8/10	The extracted fragments (e.g. headers, invoice numbers, dates) are legible and correctly recognized. Small inaccuracies or minor distortions appear sporadically.
Document Structure Recognition	7/10	The API returns a rich set of metadata, including positions, text boundaries, and element hierarchy (e.g. sections, tables). However, this requires additional mapping to application-specific fields.
Table Extraction Quality	8/10	The identified table areas are well marked and data (such as item numbers, values, item codes) are extracted with precise location and size information.
Performance and Processing Time	9/10	Adobe PDF Extract API works quickly and produces detailed results – typical of cloud solutions.
Ease of Integration and Use	8/10	Results are returned in a structured JSON format, containing detailed information about fonts, positioning, and structure – which facilitates further processing, although it may require additional mapping.
Structured Document Support	8/10	Adobe handles typical documents such as invoices, item lists, accounting notes, and customs declarations very well. Data is recognized in detail, preserving the layout and full content. However, there is no automatic semantic interpretation of fields – the user must assign labels themselves (e.g. “NIP”, “invoice date”, “net value”).

Conclusion 

Adobe PDF Extract API demonstrates high quality extraction of both text and document structure. Test results indicate that key data such as headers, tables, and metadata are precisely localized and transferred in a rich form (including information about location, fonts, and attributes). Although full interpretation of the document structure (e.g. assignment of individual fields) may require additional processing, the obtained output provides a solid basis for further integration with business systems. Overall, the tool stands out for its speed of operation and high data detail – making it a valuable solution in processing structured documents. 

Final score: 8 / 10 

#5 ABBYY FlexiCapture 

General Characteristics 

ABBYY FlexiCapture is an advanced platform for intelligent document processing that automatically recognizes, classifies and extracts data from various documents (paper, scans, PDF). It supports complex layouts (forms, invoices, tables), works in many languages (including Polish) and allows for full automation of document flow in organizations. 

ABBYY FlexiCapture Evaluation – Test on 10 Documents 

Category	Score (max 10)	Comment
OCR/Text Extraction Accuracy	9/10	High precision of content recognition, even in documents with more technical language and different structures. Special characters, numbers and address data preserved without distortion.
Document Structure Recognition	9/10	The tool perfectly identifies sections, text blocks, and the logic of the document layout (e.g. headers, footers, client/contractor data).
Table Extraction Quality	9/10	Tables are extracted as complete, with correct division into columns and values. Very good recognition of “label-value” relations in financial and customs documents.
Performance and Processing Time	9/10	ABBYY works very fast – regardless of whether you use the desktop, SDK or cloud version (e.g. Vantage). The performance is high and the results are returned within seconds. However, for very large batches it requires resource adjustments (e.g. queues or batches in the cloud version).
Ease of Integration and Use	7/10	Data export to JSON or XML is available and detailed, but requires advanced configuration of templates or scenarios (e.g. pagination).
Structured Document Support	10/10	This is an environment created specifically for working with such documents. ABBYY handles invoices, transfers, CMR, PZ, declarations, accounting notes and other B2B documents.

Conclusion 

ABBYY FlexiCapture performs excellently in tests on structured documents. It reads key data very well (amounts, numbers, dates, addresses), and the logical structure of the document (e.g. form fields, columns in tables) is reproduced very faithfully. This is an enterprise-class tool – excellent for large implementations, but requires configuration and sometimes a more advanced approach to exporting results. 

Final score: 8,8 / 10  

#6 Tesseract + Layout Parser 

General Characteristics 

Tesseract is an open-source OCR engine developed by Google, effective for printed text. It supports many languages, including Polish. Layout Parser is a library for analyzing the layout of a document (segmenting headers, tables, columns, etc.) based on deep learning. Their combination allows precise OCR while maintaining the structure of the document – such use is especially useful for complex layouts (e.g. newspapers, forms, reports). 

Tesseract + Layout Parser Evaluation – Test on 10 Documents 

Category	Score (max 10)	Comment
OCR/Text Extraction Accuracy	7/10	The text was recognized mostly correctly, although there were some typos typical of Tesseract (e.g. incorrect characters, wrong spacing between words).
Document Structure Recognition	5/10	Layout Parser identified basic sections and headings, but hierarchical structure (e.g. subsections, columns) was not fully preserved.
Table Extraction Quality	2/10	Tables were not recognized as separate structures – they were treated as continuous text without division into columns and rows.
Performance and Processing Time	8/10	The process is fast – both Tesseract and Layout Parser run locally and produce fast results.
Ease of Integration and Use	7/10	The open-source approach provides a lot of flexibility, but requires manual tuning, coding, and working with layout models.
Structured Document Support	4/10	It handles text well, but does not offer a default interpretation of form fields, invoices, or tabular data.

Conclusion 

Tesseract + Layout Parser is a solid, flexible, open-source tool that works well for OCRing simple text documents, but requires a lot of work to achieve the level of table and structure recognition comparable to commercial tools (e.g. ABBYY, Google, Adobe). However, it can be a great base for building your own OCR solutions. 

Final score: 5.5 / 10 

#7 DocTR 

General Characteristics 

DocTR is an open-source deep learning (TensorFlow/PyTorch) library for recognizing text from documents. It combines text detection and recognition in a single pipeline, supports multiple languages, and works on both images and PDFs. Ideal for local document processing, without the need for the cloud. 

DocTR Evaluation – Test on 10 Documents 

Category	Score (max 10)	Comment
OCR/Text Extraction Accuracy	10/10	Great OCR quality – the entire text was read correctly, without major errors or omissions.
Document Structure Recognition	3/10	No segmentation into logical blocks, headings, sections – text returned as a set of lines, without classification or hierarchy.
Table Extraction Quality	2/10	Tables are not detected as structures – data is broken down into a string of text without columns and rows.
Performance and Processing Time	9/10	Very fast acting – ideal for local applications.
Ease of Integration and Use	7/10	Open-source, easy to install and good documentation – but requires manual mapping to the document structure.
Structured Document Support	3/10	Text extracted well, but no support for typical structures: forms, tables, invoice fields.

Conclusion 

DocTR is a very good tool for pure OCR – it recognizes text with high accuracy and speed. However, it does not recognize the structure of the document or analyze the layout semantically. It is a good base for further processing, but requires external tools if you want to recreate the layout of tables, form fields or section classification. 

Final score: 5.7 / 10 

#8 PaddleOCR + PP-Structure 

General Characteristics 

PaddleOCR is an open-source OCR tool created by Baidu, offering high-accuracy text recognition, including for Chinese and other languages. PP-Structure is its extension module that analyzes the structure of documents – detects tables, paragraphs, titles, form fields. PaddleOCR recognizes text in images, while PP-Structure divides the document into logical blocks (e.g. tables, headers). It’s a great tool for advanced OCR that supports multiple languages, works locally or in the cloud, and allows easy integration with Python. 

PaddleOCR + PP-Structure Evaluation – Test on 10 Documents 

Category	Score (max 10)	Comment
OCR/Text Extraction Accuracy	9/10	Very good quality of text recognition – minimal number of typos. Text from forms, invoices, tables recognized correctly and in full.
Document Structure Recognition	8/10	Text sections, headers and divisions are detected sensibly. There is no full field classification (e.g. “NIP”, “amount”), but the document layout is preserved.
Table Extraction Quality	9/10	Tables recognized very well – with precise division into rows and columns. Data exported to Excel in a usable form.
Performance and Processing Time	8/10	Efficient local processing, fast analysis. Results readable and available in several formats (TXT, XLSX, HTML).
Ease of Integration and Use	7/10	Good open-source support, but requires manual configuration of pipeline and models. A bit technical to implement for less advanced users.
Structured Document Support	9/10	It handles invoices, bills of lading, customs documents very well – it even recognizes non-standard table and description layouts.

Conclusion 

PaddleOCR + PP-Structure is the best open-source OCR tool for analyzing structured documents that we have tested. It provides very good quality of text and table recognition, preserves the document structure and allows for exporting data in a convenient format (e.g. XLSX, HTML). The only limitation: no automatic semantic classification of fields (e.g. “recipient name”, “gross value”), but this can be supplemented with your own code. 

Final score: 8.3 / 10 

Summary Table – Comparison of OCR and Layout Analysis Tools 

Tool	OCR (10)	Structure (10)	Tables (10	Performance (10)	Integration (10)	Structured docs (10)	Final score
ABBYY	9	9	9	9	7	10	8.8 / 10
PaddleOCR + PP-Structure	9	8	9	8	7	9	8.3 / 10
Amazon Textract	8	7	8	9	8	8	8.0 / 10
Adobe PDF Extract API	8	7	8	9	8	8	8.0 / 10
Google Document AI	8	7	8	9	8	8	8.0 / 10
Azure Form Recognizer	10	6	4	9	8	6	7.2 / 10
DocTR	10	3	2	9	7	3	5.7 / 10
Tesseract + Layout Parser	7	5	2	8	7	4	5.5 / 10

Conclusions and Recommendations 

Best Overall Quality: ABBYY 

• Most accurate text and structure recognition. 
• Great support for tables and financial and logistics documents. 
• Ideal choice for production environments with a large number of documents with a repeatable structure. 

Best Open-Source: PaddleOCR + PP-Structure 

• Ready to use, easy to integrate. 
• Excellent OCR and table recognition quality while maintaining complete independence from commercial APIs. 
• Ideal base for a proprietary enterprise-class solution. 

Best SaaS/API Solutions: Amazon / Google / Adobe 

• Automatically detect document sections, although not always semantically precisely. 
• Some (like Adobe) offer very good layout mapping in PDF. 

Best Pure OCR Quality: DocTR 

• Great text recognition. 
• No semantics or tables – for applications where only content matters. 

Best OCR with Layout but No Semantics: Azure 

• Very good OCR + position detection. 
• No data interpretation (e.g. tables or fields) – requires a proprietary analysis layer. 

Recommendation Depending on Needs 

Need	Recommended Tool
Top quality and production ready	✅ ABBYY
Open-source, full control	✅ PaddleOCR + PP-Structure
Fast implementation via API	✅ Amazon Textract / Google Document AI
Local analysis with good OCR	✅ DocTR
Best OCR + text positions (for parsing)	✅ Azure Form Recognizer