Optical character recognition (OCR) is the process of turning an image containing text, such as scanning a page, receipt, or photo. Systems initially based on brittle rules have developed to a rich ecosystem that can read visual models of complex, multilingual and handwritten documents.
How OCR works?
Each OCR system meets three core challenges:
- Test – Find where text appears in the image. This step must handle skewed layouts, curved texts, and chaotic scenes.
- make out – Convert detected areas to characters or words. Performance depends to a large extent on how the model handles low resolution, font diversity, and noise.
- Post-processing – Use a dictionary or language model to correct identification errors and preserve structures, whether they are table cells, column layouts, or form fields.
Difficulties are getting bigger and bigger when dealing with handwriting, scripts other than Latin letters, or highly structured documents such as invoices and scientific papers.
From handmade pipes to modern architecture
- Early OCR: Rely on binaryization, segmentation and template matching. Valid only for clean printed text.
- Deep Learning: CNN-based and RNN-based models eliminate the need for manual functional engineering, thus achieving end-to-end identification.
- transformer: Architectures such as Microsoft’s Trocr extend OCR to handwriting recognition and multilingual settings, and improve generalization.
- Visual Model (VLM): Large multi-model models such as QWEN2.5-VL and LLAMA integrate OCR with contextual reasoning, not only dealing with text, but also charts, tables and mixed content.
A more advanced open source OCR model
| Model | architecture | Advantages | The most suitable |
|---|---|---|---|
| Tserac | Based on LSTM | Mature, supports more than 100 languages, widely used | Batch digitization of printed text |
| Easyocr | Pytorch CNN + RNN | Easy to use, GPU enabled over 80 languages | Quick prototype, lightweight mission |
| Padlock | CNN + Transformer Pipeline | Strong Chinese/English support, table and recipe extraction | Structured multilingual documentation |
| doctrine | Modularity (DBNET, CRNN, VITSTR) | Flexible, support for Pytorch & TensorFlow | Research and custom pipelines |
| Troc | Based on transformer | Excellent handwriting recognition, powerful summary | Handwritten or mixed-blood input |
| qwen2.5-vl | Visual Language Model | Context-aware, processing graphs and layouts | Complex documentation for mixed media |
| Camel 3.2 Vision | Visual Language Model | OCR integrated with inference tasks | Quality inspection scan documents, multi-mode tasks |
Emerging Trends
Research on OCR is moving in three famous directions:
- Unified Model: Systems in systems such as Vista-Oct crash detection, identification and spatial positioning, thus reducing error propagation.
- Low resource language: Benchmarks such as PSOCR emphasize performance gaps in languages such as Pashto, and multilingual fine-tuning is recommended.
- Efficiency optimization: Models such as TexThawk2 reduce visual token counts in transformers, cutting inference costs without losing accuracy.
in conclusion
The open source OCR ecosystem provides options to balance accuracy, speed and resource efficiency. Tesseract can still draw printed text through structured and multilingual documents, while Trocr pushes the boundaries of handwriting recognition. For raw text other than use cases that require document understanding, vision model 3.2 vision such as QWEN2.5-VL and LLAMA are promising, although expensive to deploy.
The right choice does not depend on the accuracy of the rankings, but more on the reality of the deployment: the type of documentation, scripts, and structural complexity you need to deal with, and the available compute budget. Benchmarking candidate models based on your own data is still the most reliable way to decide.
Michal Sutter is a data science professional with a master’s degree in data science from the University of Padua. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels in transforming complex datasets into actionable insights.
