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.
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
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
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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.
Comparative Summary
Krishna Purwar
You can find me exploring niche topics, learning quirky things and enjoying 0 n 1s until qbits are not here-
