Optical Character Recognition (OCR) converts images of text into machine-readable text, which is crucial for various applications like data extraction and document digitization. Tesseract, a powerful OCR solution, began as an HP project, became open-source in 2005, and is now maintained by global developers. This article explores Tesseract’s benefits, workings, and usage examples.
Why use Tesseract API?
1. Wide Language Support
Tesseract can recognize text in over 100 languages, making it ideal for global applications with diverse language requirements.
2. Open-Source
Available under the Apache 2.0 license, Tesseract is free for commercial use. Developers can access, modify, and contribute to its source code, fostering a collaborative community that enhances its capabilities. Although Google stopped maintaining it in 2018, open-source developers have continued its development, with version 5.0.0 released in November 2021.
3. Wrappers like Pytesseract
These simplify Tesseract’s use by offering high-level interfaces in languages like Python, reducing the learning curve and facilitating quicker development and prototyping.
How does Tesseract work?
Tesseract Timeline
As of version 5.3.2, Tesseract uses LSTM-based architecture from version 4.0.0 onwards.
LSTM Architecture
Long-Short Term Memory (LSTM) is a type of RNN architecture that handles long-term dependencies and addresses the vanishing gradient problem by using cell states and various gates.
Legacy Tesseract 3.x Process:
- Input: Takes a pre-processed image with clear text regions.
- Connected Component Analysis: Breaks the image into individual parts that form letters and symbols.
- Blobs and Lines: Groups these parts into “blobs” and organizes blobs into lines of text.
- Word Segmentation: Splits lines into separate words based on character spacing.
- Two-step Recognition:
a. First Pass: Attempts to recognize words.
b. Second Pass: Uses the first pass results as training examples for improved recognition.
6. Correction Pass: Fixes mistakes from the first pass.
7. Final Adjustments: Fine-tunes word spacing and identifies small capital letters.
Modern Tesseract: The modernization involved cleaning code and incorporating the LSTM model. The input image is processed line by line in boxes, feeding into the LSTM model for output. This approach enhances accuracy and efficiency.
Limitations of Tesseract
Tesseract, though powerful, has several limitations:
- Preprocessing Dependency: Requires meticulous preprocessing for optimal results, which can be challenging due to varying image quality and conditions.
- Scanned Images: Less effective with scanned documents, often struggling with artifacts and skewed text.
- Complex Layouts: Struggles with intricate layouts, multi-column text, and unconventional arrangements.
- Handwriting Recognition: Not well-suited for handwritten text, performing best with printed text.
- Language and Fonts: Performance can fluctuate with less common languages and fonts.
- Gibberish Output: May produce gibberish as OCR output, affecting data accuracy.
- Customization Complexity: Customizing requires understanding parameters involving trial and error.
- Resource Intensive: High processing demands impact speed and resource consumption.
Conclusion
OCR technology has evolved significantly from the 1914 OPTOPHONE to advanced deep learning. Tesseract performs well with clean, properly aligned, high-quality images. With its rich history and continuous development, the Tesseract API is a versatile, open-source OCR solution supporting over 100 languages. We hope this article has provided a clear understanding of how to use Tesseract.
Refer to the full article by docsumo here
