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Install the external Tesseract engine and the trained language data first, then install the Python packages in the same environment that runs your script:
python -m pip install Pillow pytesseractTesseract supports JPEG input through its image-reading layer. The example below handles ordinary printed English text; change lang when the matching trained data is installed.
1. Install the OCR engine and Python libraries
Install Tesseract separately
pytesseract is a Python wrapper; it does not contain the Tesseract executable. Install Tesseract for your operating system using the current instructions for that environment, and install the trained data for every language you plan to recognize. The requested language code must match an installed data file.
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After installation, verify that the tesseract executable is available on your system PATH. If it is not, you can point pytesseract at the executable explicitly in Python.
Install Pillow and pytesseract
python -m pip install Pillow pytesseractUse the same Python interpreter for installation and execution. A common cause of import errors is installing into one virtual environment and running the script with another.
2. Extract plain text from a JPG
Save this as extract_text.py beside scan.jpg:
from PIL import Image
import pytesseract
image = Image.open("scan.jpg")
text = pytesseract.image_to_string(image, lang="eng")
print(text)Run it with:
python extract_text.pyimage_to_string() returns a normal Python string. Tesseract may include line breaks and a trailing newline, so trim only when that is appropriate for your application:
clean_text = text.strip()
print(clean_text)Use an explicit executable path when PATH is not configured
If pytesseract reports that it cannot find Tesseract, set tesseract_cmd before calling OCR. Replace the path with the executable location on your machine:
from PIL import Image
import pytesseract
pytesseract.pytesseract.tesseract_cmd = r"C:PathTotesseract.exe"
image = Image.open("scan.jpg")
print(pytesseract.image_to_string(image, lang="eng"))On macOS or Linux, use the full path returned by your package manager or shell. Do not point to a directory; point to the Tesseract executable itself.
3. Select the correct language
The lang argument is a plus-separated list of installed languages. For example, eng+fra asks Tesseract to use English and French data:
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text = pytesseract.image_to_string(image, lang="eng+fra")If the requested trained data is missing, Tesseract raises an error or cannot recognize the intended language. Install the matching data and check that Tesseract can see its data directory before changing Python code.
4. Improve difficult JPGs without assuming one universal recipe
Tesseract performs image processing internally, but poor focus, low contrast, skew, heavy JPEG artifacts, and decorative layouts can still reduce recognition quality. Inspect the original image first, then test one change at a time on representative files.
Grayscale and contrast
from PIL import Image, ImageOps
import pytesseract
image = Image.open("scan.jpg")
gray = ImageOps.grayscale(image)
contrast = ImageOps.autocontrast(gray)
text = pytesseract.image_to_string(contrast, lang="eng")
print(text)Autocontrast can help some scans and hurt others. Keep the original and compare the resulting text rather than applying it blindly.
Thresholding
from PIL import Image, ImageOps
import pytesseract
image = ImageOps.grayscale(Image.open("scan.jpg"))
thresholded = image.point(lambda pixel: 255 if pixel > 180 else 0)
print(pytesseract.image_to_string(thresholded, lang="eng"))The threshold value is image-dependent. Test several values only when the source has uneven lighting or a faint background; a hard threshold can erase thin characters.
Choose a page-segmentation mode for the layout
Tesseract’s page-segmentation setting tells it what kind of layout to expect. A block of text, a single line, and a sparse label are different problems. Pass a configuration string and compare outputs:
config = "--psm 6" # one uniform block of text
text = pytesseract.image_to_string(image, lang="eng", config=config)There is no universally best mode. Try a mode that matches the actual page, and validate it on your own images, especially when columns, captions, or isolated labels are present.
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5. Process more than one JPG
For a directory of images, keep failures attached to their filenames so one bad file does not hide the rest:
from pathlib import Path
from PIL import Image
import pytesseract
for path in sorted(Path("images").glob("*.jpg")):
try:
with Image.open(path) as image:
text = pytesseract.image_to_string(image, lang="eng")
print(f"n--- {path.name} ---n{text}")
except Exception as exc:
print(f"{path.name}: {exc}")Use an additional glob for uppercase extensions if needed, such as *.JPG. Keep the source filename with the OCR result when creating searchable archives or downstream records.
6. Choose structured output when plain text is not enough
TSV data with word positions
TSV output includes recognized words and fields such as confidence and bounding-box coordinates. It is useful for highlighting text or selecting a region:
from PIL import Image
import pytesseract
from pytesseract import Output
image = Image.open("scan.jpg")
data = pytesseract.image_to_data(
image,
lang="eng",
output_type=Output.DICT,
)
for i, word in enumerate(data["text"]):
if word.strip():
print({
"text": word,
"confidence": data["conf"][i],
"left": data["left"][i],
"top": data["top"][i],
"width": data["width"][i],
"height": data["height"][i],
})Confidence values are signals for review, not proof that a word is correct. Set your own acceptance rules for the document type.
hOCR for layout-aware HTML
hocr_bytes = pytesseract.image_to_pdf_or_hocr(
image,
lang="eng",
extension="hocr",
)
with open("scan.hocr", "wb") as output:
output.write(hocr_bytes)hOCR preserves positional information in an HTML-like format. Use it when a later process needs lines, words, or coordinates rather than a single string.
Searchable PDF
pdf_bytes = pytesseract.image_to_pdf_or_hocr(
image,
lang="eng",
extension="pdf",
)
with open("scan-searchable.pdf", "wb") as output:
output.write(pdf_bytes)These outputs are documented alternatives to plain text. Pick the smallest representation that satisfies the next step in your pipeline.
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7. Troubleshooting common failures
“No module named PIL” or “No module named pytesseract”
The packages are missing from the interpreter running the script. Install them with that interpreter:
python -m pip install Pillow pytesseractIn a virtual environment, activate it first and rerun the command.
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The Python wrapper is present, but the external engine is absent or not on PATH. Install Tesseract, reopen your terminal so environment changes take effect, or assign pytesseract.pytesseract.tesseract_cmd to the full executable path.
Missing language-data errors
The lang value does not correspond to installed trained data, or Tesseract cannot locate that data directory. Install the requested language and verify the engine’s configured data location. Start with eng only after confirming English data is available.
The script cannot open the JPG
A .jpg suffix does not guarantee valid JPEG bytes. Confirm that the file is complete and that its actual encoding is supported. Try opening it with an image viewer or re-exporting it from the source application before debugging OCR settings.
The output is empty or inaccurate
- Zoom in on the source and confirm the printed characters are genuinely legible.
- Verify the language and page-segmentation assumptions.
- Try grayscale, contrast adjustment, or thresholding on a copy.
- Check that the text is printed rather than handwriting; this workflow is not a guarantee for handwriting recognition.
- Use TSV or hOCR to inspect where Tesseract placed words when reading order looks wrong.
8. Reliability, performance, and operational considerations
Image quality dominates results
OCR cannot recover characters that are missing, blurred, clipped, or hidden by compression artifacts. Preserve the highest-quality source available, avoid repeatedly re-saving JPGs, and inspect a sample of outputs before trusting an automated batch.
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Memory and runtime
Large, high-resolution images contain more pixels for Tesseract to process. Resize only when the original is unnecessarily large and the characters remain legible; downscaling small text can remove critical detail. For repeated jobs, load one image at a time and write results incrementally instead of keeping an entire directory in memory.
Make failures observable
Record the filename, language, configuration, and exception for each job. Keep the original image beside the extracted result so a reviewer can compare questionable text with the source. For regulated or high-consequence records, add a human review step rather than treating OCR confidence as a correctness guarantee.
Or skip the browser setup
If the JPG must first be captured from a webpage, ScreenshotNeo can create a clean image before you run the OCR code above. It accepts consent banners as a visitor and removes more than 60 known consent platforms, newsletter popups, and chat widgets; each cleanup step can be disabled. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed, and the response identifies the page verdict and billing status in X-Page-Verdict and X-Billed headers. It is a capture service, not an OCR engine, so pass the returned image to Tesseract afterward.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://example.com -o shot.webpPython:
import requests
r = requests.get(
"https://api.screenshotneo.com/v1/shot",
params={"access_key": "YOUR_API_KEY", "url": "https://example.com"},
timeout=90,
)
r.raise_for_status()
open("shot.webp", "wb").write(r.content)Node.js:
const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://example.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);
if (!res.ok) throw new Error(`HTTP ${res.status}`);
const fs = await import('node:fs/promises');
await fs.writeFile('shot.webp', Buffer.from(await res.arrayBuffer()));See the ScreenshotNeo API documentation for capture parameters. Its MCP server exposes take_screenshot, get_page_info, and capture_pdf to Claude, Cursor, and other MCP clients. One thousand screenshots per month are free with no card; paid plans start at $5 for 3,000 shots, and every feature is included on every plan. Sign up for the free ScreenshotNeo plan.
FAQ
Frequently Asked Questions
Can OCR reliably read text arranged in columns?
Not always. Reading order depends on the page-segmentation model and the image layout. Test a matching --psm configuration and inspect TSV or hOCR coordinates when column order matters.
Does a high confidence value prove that a word is correct?
No. Tesseract confidence is a review signal. For important records, compare flagged words with the JPG and use a human approval step.
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