*Understanding Tesseract OCR and --psm: Why Removing It Can Improve Accuracy for Scanned Books* Introduction
Tesseract OCR is a powerful tool for extracting text from images, but selecting the right parameters is critical for accuracy. One commonly misunderstood parameter is --psm (Page Segmentation Mode). In this guide, we'll discuss a real-world issue where --psm caused incorrect text extraction for scanned books and how removing it led to *better results*. The Problem: Mixed Text from Two Facing Pages Many scanned books and documents contain *two facing pages in a single image*. When processed with --psm, Tesseract sometimes *misinterprets the structure*, causing the extracted text to be jumbled. Instead of reading one page at a time, Tesseract would mix text from both pages, extracting: Line 1 from right page + Line 1 from left page Line 2 from right page + Line 2 from left page ... This happens because --psm forces Tesseract to assume a specific layout, which can *conflict with the actual structure of the scanned document*. The Solution: Removing --psm By removing --psm, Tesseract *processed the right page first in order*, then moved to the left page. This resulted in a natural reading order and a significantly better OCR result: Line 1 from right page Line 2 from right page ... (Line 1 from left page follows after the right page is complete) This confirms that *in some cases, manually setting --psm can do more harm than good*. When to Avoid --psm - When processing scanned *books or documents with two facing pages*. - When *text is misaligned or mixed* in the OCR output. - When dealing with *complex layouts* where Tesseract's automatic handling works better. When to Use --psm There are cases where --psm is still useful, such as: - Single-column printed text (--psm 6) - Sparse text (--psm 11) - Images containing only a single word (--psm 8) Recommended OCR Settings For scanned books or multi-column text, a safer approach is: pytesseract.image_to_string(image, config='--oem 1 -c preserve_interword_spaces=1') This avoids forcing a layout assumption while keeping Tesseract optimized for text extraction. Users can specify the language(s) as needed (e.g., -l eng, -l ara, or -l ara+eng). Conclusion This discovery highlights why *experimentation is key* when working with OCR. If your text output appears mixed or out of order, try *removing --psm* and letting Tesseract handle the layout automatically. Hopefully, this guide helps others facing similar issues! Have you encountered other OCR challenges? Share your experience in the comments or discussion forums! -- You received this message because you are subscribed to the Google Groups "tesseract-ocr" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion visit https://groups.google.com/d/msgid/tesseract-ocr/b52ea3c1-5dda-4ba3-9034-98a89718bc88n%40googlegroups.com.
