OCR technology has transformed how document analysis is performed, allowing text to be extracted from images and converted into formats computers can understand. I’ve seen this unlock everything from faster data entry to searching large scanned archives.
In the last few years, OCR has advanced rapidly with newer deep learning models, pushing its capabilities far beyond what was previously possible. In this guide, I’m comparing some of the most advanced OCR models available today based on how they actually perform, highlighting their strengths, limitations, and real-world behavior.
Mistral OCR Analysis
Mistral OCR is an Optical Character Recognition API focused on document understanding. While testing it, I noticed that it attempts to interpret multiple document elements such as text, tables, equations, and media together rather than treating them in isolation. It takes images and PDFs as input and extracts content in an ordered interleaved text and images
Strengths
- High accuracy (around 90%) when clear images are provided
- Works across multiple file formats like PDF and JPG
- Reliable results for standard printed text based on my tests
Weaknesses
- No confidence score, which meant I had to manually verify outputs
- Limited support for multilingual text
- Struggled with some handwritten text fields
- Performance dropped noticeably when the input image quality was low
Conclusion
- The tool does not provide a confidence score, so I had to manually verify whether the extracted output was correct.
- Overall, if clear images are provided, the tool can extract 90% of the text.
- The tool was able to recognize text well 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. |
OLM OCR Analysis
olmOCR is an open-source OCR tool designed for high-throughput conversion of PDFs and documents into plain text. I focused on how well it preserved reading order and handled structured content during testing. It supports tables, equations, handwriting, and more.
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Strengths
- Around 90% text extraction accuracy with clear images in my tests
- Good compatibility with PDF and JPG files
- Consistent performance with standard printed text
Weaknesses
- No confidence score, so results needed manual checking
- Limited multilingual recognition
- Handwritten text extraction was inconsistent
- 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 |
Conclusion
- The tool is not providing a confidence score so we have to check manually that the output is correct or not.
- Overall, when I tested it with clear images, the tool was able to extract close to 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 OCR approach where the model behaves more like an agent. While testing it, I observed that it could handle complex extraction tasks when everything worked as expected. This often involves combining OCR with other AI capabilities.
Strengths
- Highly flexible across different document formats based on my usage
- Capable of handling complex extraction tasks when successful
- Robust to variations and noise in documents.
- When it works, it's really good.
Weaknesses
- Slow compared to other models I tested
- For some files, it failed to return any output at all
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 |
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.
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Strengths
- Fast in execution and worked reasonably well with plain text in my tests.
Weaknesses
- Did not preserve table structure properly
- Could not analyze figures or image-based content
| 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 |
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 |
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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