OCR technology has transformed the way documents are processed, allowing text to be extracted from images and converted to a readable format for computers, and this has opened up a variety of applications, from data entry to searching scanned archives. In the last few years, OCR has seen dramatic advances, driven by the advent of new deep learning models, therefore this has extended the scope of OCR to previously unthought-of levels. In this blog, we will highlight some of the most advanced OCR models available on the market today, and compare their capabilities, strengths and weaknesses, thus providing a comprehensive overview of the current state of OCR technology.
Mistral OCR Analysis
Mistral OCR is an Optical Character Recognition API that sets a new standard in document understanding. Unlike other models, Mistral OCR comprehends each element of documents—media, text, tables, equations with unprecedented accuracy and cognition. It takes images and PDFs as input and extracts content in an ordered interleaved text and images
Strengths
High accuracy (90%) with clear images
Versatile file format compatibility (PDF, JPG)
Reliable performance with standard printed text
Weaknesses
No confidence scoring mechanism, requiring manual verification
Limited multilingual text recognition capabilities
Struggles with handwritten text extraction
Requires high-quality input images for optimal performance
Conclusion
The tool is not providing a confidence score so we have to check manually that the output is correct or not.
Overall, if clear images are provided, the tool can extract 90% of the text.
The tool was able to recognize text good in multiple file formats (tested with PDF and JPG)
The weakness is in the multilingual text recognition.
The tool had trouble extracting some handwritten text from fields.
Test Case Description
Input
Status
Notes
Text Extraction from Scanned Document
Scanned image of a multi-page document
Good - Extracted 90% of the text.
Text Extraction from Scanned Document
Scanned image of a multi-table document
Good - was able to extract 90% of the data
Text Extraction from PDF
A PDF document with text and images
Bad - was able to recognize only 30% of the words
Multilingual Document
Document containing text in multiple languages
Fail
Not able to recognize multilingual doc’s properly.
Table Extraction
Document containing tables
Bad
Handwriting Recognition
Image of handwritten text
Good
Performance is ok, was able to recognize 70% of the text. Was not
able to recognize some words
Pure Text Doc
PDF on scanned text
Excellent
Image Data Extraction
Image with text data inside it.
Bad
Some details are represented as images (img-0.jpeg, img-1.jpeg,
etc.), which means the numeric values are missing from the extracted
text.
Text Extraction from Scanned Document
Input
Scanned image of a multi-page document
Status
Good - Extracted 90% of the text.
Notes
Text Extraction from Scanned Document
Input
Scanned image of a multi-table document
Status
Good - was able to extract 90% of the data
Notes
Text Extraction from PDF
Input
A PDF document with text and images
Status
Bad - was able to recognize only 30% of the words
Notes
Multilingual Document
Input
Document containing text in multiple languages
Status
Fail
Notes
Not able to recognize multilingual doc’s properly.
Table Extraction
Input
Document containing tables
Status
Bad
Notes
Handwriting Recognition
Input
Image of handwritten text
Status
Good
Notes
Performance is ok, was able to recognize 70% of the text. Was not
able to recognize some words
Pure Text Doc
Input
PDF on scanned text
Status
Excellent
Notes
Image Data Extraction
Input
Image with text data inside it.
Status
Bad
Notes
Some details are represented as images (img-0.jpeg, img-1.jpeg,
etc.), which means the numeric values are missing from the extracted
text.
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OLM OCR Analysis
olmOCR is an open-source tool designed for high-throughput conversion of PDFs and other documents into plain text while preserving natural reading order. It supports tables, equations, handwriting, and more.
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Strengths
90% text extraction accuracy with clear images
Good compatibility with multiple file formats (PDF, JPG)
Reliable performance with standard text
Weaknesses
No confidence scoring mechanism
Requires manual verification of results
Limited multilingual text recognition
Poor handwritten text extraction capabilities
Dependent on image clarity for optimal performance
Test Case Description
Input
Expected Output
Status
Notes
Text Extraction from Scanned Document
Scanned image of a multi-page document
Accurate extraction of all text, maintaining page order
Good
Test basic OCR functionality.
Text Extraction from Scanned Document
Scanned image of a multi-table document
Proper extraction of all the details in the doc.
Good - was able to extract 90% of the data
Text Extraction from PDF
PDF document with text and images
Accurate extraction of text and embedding of images
Good
Test OCR on PDF files.
Multilingual Document
Document containing text in multiple languages
Accurate extraction of text in all languages
Fail
Not able to recognize multilingual doc’s properly.
Table Extraction
Document containing tables
Accurate extraction of table data in a structured format.
Good
Was able to extract the text data from the table
Form Data Extraction
Scanned form with filled-in data
Accurate extraction of form fields and values
Very Good.
The model was able to extract most of the data accurately,
impressive.
Handwriting
Recognition
Image of handwritten text
Accurate transcription of handwritten text
OK
Performance is ok, was able to recognize 70% of the text. Was not
able to recognize some words
Text Extraction from Scanned Document
Input
Scanned image of a multi-page document
Expected Output
Accurate extraction of all text, maintaining page order
Status
Good
Notes
Test basic OCR functionality.
Text Extraction from Scanned Document
Input
Scanned image of a multi-table document
Expected Output
Proper extraction of all the details in the doc.
Status
Good - was able to extract 90% of the data
Notes
Text Extraction from PDF
Input
PDF document with text and images
Expected Output
Accurate extraction of text and embedding of images
Status
Good
Notes
Test OCR on PDF files.
Multilingual Document
Input
Document containing text in multiple languages
Expected Output
Accurate extraction of text in all languages
Status
Fail
Notes
Not able to recognize multilingual doc’s properly.
Table Extraction
Input
Document containing tables
Expected Output
Accurate extraction of table data in a structured format.
Status
Good
Notes
Was able to extract the text data from the table
Form Data Extraction
Input
Scanned form with filled-in data
Expected Output
Accurate extraction of form fields and values
Status
Very Good.
Notes
The model was able to extract most of the data accurately,
impressive.
Handwriting
Recognition
Input
Image of handwritten text
Expected Output
Accurate transcription of handwritten text
Status
OK
Notes
Performance is ok, was able to recognize 70% of the text. Was not
able to recognize some words
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Conclusion
The tool is not providing a confidence score so we have to check manually that the output is correct or not.
Overall, if clear images are provided, the tool can extract 90% of the text.
The tool was able to recognize text good in multiple file formats (tested with PDF and JPG)
The weakness is in the multilingual text recognition.
The tool had trouble extracting some handwritten text from fields.
Agentic Document Extraction Analysis
Agentic Document Extraction represents a newer paradigm in OCR, where the model acts as an "agent" that can intelligently navigate and extract information from documents. This often involves combining OCR with other AI capabilities.
Strengths
Highly flexible and adaptable to diverse document formats.
Can perform complex extraction tasks, such as identifying key-value pairs or summarizing content.
Robust to variations and noise in documents.
When it works, it's really good.
Weaknesses
Slow.
For some files, it does not work, hence no output.
Additional Notes: If issues can be fixed, it works really well.
Comparison Table
File
Time
Quality
Multilungual Handwriting
Recognition
30 sec
Okayish - identified telugu as kannad, good with hindi
Table Extraction
1 min 30 sec
Good
Text Extraction from Scanned Document
1 min 38 sec
Good
Text Extraction from Scanned Document
1 min
Good
Form Data Extraction
4 min 13 sec
Error, did not give anything
Table Extraction
1 min 30 sec
Good, 100% accuracy
Form Data Extraction
4 min
Error, did not give anything
Form Data Extraction
2 min 50 sec
Good, 100% accuracy
Handwriting
Recognition
46 sec
Good, 100% accuracy
Multilungual Handwriting
Recognition
Time
30 sec
Quality
Okayish - identified telugu as kannad, good with hindi
Table Extraction
Time
1 min 30 sec
Quality
Good
Text Extraction from Scanned Document
Time
1 min 38 sec
Quality
Good
Text Extraction from Scanned Document
Time
1 min
Quality
Good
Form Data Extraction
Time
4 min 13 sec
Quality
Error, did not give anything
Table Extraction
Time
1 min 30 sec
Quality
Good, 100% accuracy
Form Data Extraction
Time
4 min
Quality
Error, did not give anything
Form Data Extraction
Time
2 min 50 sec
Quality
Good, 100% accuracy
Handwriting
Recognition
Time
46 sec
Quality
Good, 100% accuracy
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GOT-OCR-2.0-hf Analysis
GOT-OCR-2.0-hf (referring to a model from the GOT family, made available on Hugging Face) is another notable OCR model.
Strengths
Fast, works with normal text.
Weaknesses
Does not store columns/tables properly.
Cannot analyze figures.
S. No.
File Name
Time (sec)
Quality
Comment
Form Data Extraction
65.38
Bad
Cannot understand table
Form Data Extraction
85.13
Bad
Cannot understand table
Text Extraction from Scanned Document
6.09
Good
Missed the signature
Form Data Extraction
64.72
Bad
Cannot understand table
Table Extraction
3.56
Bad
Have everything but not in proper format
Form Data Extraction
159.78
Bad
Cannot understand table
Text Extraction from Scanned Document
81.65
Bad
Good until it came across figure
File Name
Form Data Extraction
Time (sec)
65.38
Quality
Bad
Comment
Cannot understand table
File Name
Form Data Extraction
Time (sec)
85.13
Quality
Bad
Comment
Cannot understand table
File Name
Text Extraction from Scanned Document
Time (sec)
6.09
Quality
Good
Comment
Missed the signature
File Name
Form Data Extraction
Time (sec)
64.72
Quality
Bad
Comment
Cannot understand table
File Name
Table Extraction
Time (sec)
3.56
Quality
Bad
Comment
Have everything but not in proper format
File Name
Form Data Extraction
Time (sec)
159.78
Quality
Bad
Comment
Cannot understand table
File Name
Text Extraction from Scanned Document
Time (sec)
81.65
Quality
Bad
Comment
Good until it came across figure
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Comparative Summary
Model Name
Mistral OCR
OLM OCR
Agentic Document Extraction
GOT-OCR-2.0-hf
Pros
Excellent is text data extraction
If clear tabular data is provided, extraction is good.
If clear images are provided, the extraction is good. Good in Form data extraction Good in Tabular data extraction
When works, it's really good.
Fast, works with normal text.
Cons
Weak in extracting text from images.
sometimes, Weak in Tabular data extraction with low quality pdf.
Weak in multi lingual data detection.
Does not provide confidence score. Weak in multilingual text detection
Slow, sometimes if it does not work, it does not give any output.
Does not store columns / tables properly. Cannot analyse figure into figure.
Additional Notes
Some details are represented as images (img-0.jpeg, img-1.jpeg, etc.), which means the numeric values are missing from the extracted text.
Does not work for some files, if we can fix that, it works really well.
Type
Closed Source
Open Source
Closed Source
Open Source
Pros
Mistral OCR
Excellent is text data extraction
If clear tabular data is provided, extraction is good.
OLM OCR
If clear images are provided, the extraction is good. Good in Form data extraction Good in Tabular data extraction
Agentic Document Extraction
When works, it's really good.
GOT-OCR-2.0-hf
Fast, works with normal text.
Cons
Mistral OCR
Weak in extracting text from images.
sometimes, Weak in Tabular data extraction with low quality pdf.
Weak in multi lingual data detection.
OLM OCR
Does not provide confidence score. Weak in multilingual text detection
Agentic Document Extraction
Slow, sometimes if it does not work, it does not give any output.
GOT-OCR-2.0-hf
Does not store columns / tables properly. Cannot analyse figure into figure.
Additional Notes
Mistral OCR
Some details are represented as images (img-0.jpeg, img-1.jpeg, etc.), which means the numeric values are missing from the extracted text.
OLM OCR
Agentic Document Extraction
Does not work for some files, if we can fix that, it works really well.
GOT-OCR-2.0-hf
Type
Mistral OCR
Closed Source
OLM OCR
Open Source
Agentic Document Extraction
Closed Source
GOT-OCR-2.0-hf
Open Source
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Krishna Purwar
You can find me exploring niche topics, learning quirky things and enjoying 0 n 1s until qbits are not here-
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