esafwan/Multi-StagePDF_OCRPipeline.md

## Multi-StagePDF_OCRPipeline.md

      
    Raw
  

              Multi-StagePDF_OCRPipeline.md
            
          
    Multi-Stage PDF Processing Pipeline (Open-Source Tools)

Goal: Build a Python pipeline on Linux/macOS that processes PDFs in stages: removing headers/footers, detecting signatures (digital and handwritten), assessing page quality, performing OCR (skipping certain regions), and optionally falling back to cloud vision models for very complex pages. Below are recommended open-source tools/models for each stage, with setup/usage notes and integration tips.
Stage 1: Header/Footer Detection and Removal

Repetitive headers, footers, or page numbers can interfere with content extraction. The pipeline’s first step is to detect and strip these recurring elements:


PDF Text Parsing: Use a PDF parser like PyMuPDF (Python binding fitz) or pdfplumber to extract text with coordinates. These libraries let you analyze text positions on each page. For example, with PyMuPDF you can iterate through pages and retrieve text blocks or individual text spans with their bounding boxes. By analyzing top/bottom regions or finding text lines that appear on most pages, you can identify likely header/footer strings. In practice, you might collect all text lines and use a frequency count or position-based heuristic (e.g. any text within the top 5% or bottom 5% of the page that repeats across many pages) and filter those out. PyMuPDF even exposes font info (size, font name) for text elements – if headers use a distinct font size, that can be a clue.


Example Approach: One strategy is to use PyMuPDF’s text extraction:
import fitz
doc = fitz.open("document.pdf")
header_lines = {}
for page in doc:
    blocks = page.get_text("blocks")  # list of (bbox, text, block_type)
    for (x0,y0,x1,y1,text,block_type) in blocks:
        # Identify text near top (small y0) or bottom (y1 near page height)
        if y0 < 100 or page.rect.height - y1 < 100:  
            header_lines[text.strip()] = header_lines.get(text.strip(), 0) + 1
# Determine frequent header/footer text
common = [txt for txt,count in header_lines.items() if count > 1]
This pseudo-code tallies text in the top/bottom 100px of pages. In a real implementation you’d refine the thresholds or matching (e.g. ignore page numbers by pattern). If the PDF has an OCR text layer (for scanned pages), the same method applies – repeated OCR text at top/bottom indicates headers/footers to ignore.


Alternate Tools: The pypdf library (successor to PyPDF2) supports a callback to ignore text by position. For instance, you can supply a custom visitor_text function to page.extract_text() that appends text only if its Y-coordinate is within a certain range. This requires knowing approximate header/footer bounds upfront, but it’s effective if margins are consistent. Another option is pdfplumber’s Page.crop() to cut off top/bottom regions before text extraction (you must specify coordinates for the crop box). These methods effectively remove unwanted text from the output.


Visual Headers: If the PDF pages are images (scans) with a repeated logo or letterhead, detecting that requires image processing. You could use OpenCV to compare the top strip of pixels across pages – e.g. compute a hash or difference image to see if the same graphic appears on every page. For instance, extract the top 10% of each page image and use a perceptual hash to find duplicates. If a common image/header is found, you can crop it out or mask it. This ensures subsequent OCR doesn’t read the header text or artifacts.


Why these tools? PyMuPDF and pdfplumber are robust, self-hosted libraries for PDF parsing. PyMuPDF is very fast in C and handles both vector text and images; pdfplumber (built on pdfminer) provides high-level text extraction and layout info. Both allow coordinate-based filtering, crucial for isolating headers/footers. Using them in Python is straightforward (install via pip). By removing or flagging repeated top/bottom content, you ensure the OCR/text extraction in later stages focuses on the document’s main body (not clutter from page titles or numbers).
Stage 2: Digital Signature Field Detection

Next, identify pages that contain embedded digital signatures (e.g. DocuSign signatures or PDF AcroForm signature fields). Digital signatures are usually PDF form fields of type /Sig or special annotations indicating a signed document. To detect these:


PDF Libraries for Form Fields: PyMuPDF and pypdf can inspect a PDF’s interactive form data. For example, PyMuPDF exposes a document property to check for any signature fields. Using PyMuPDF’s Document object, you can call doc.form_signatures or related methods – PyMuPDF can report if the PDF has signature fields (it reads the PDF’s /SigFlags). A return value of 1 typically means one or more signature fields exist, and 3 can indicate the document is actually signed (contents locked against changes).


Locating the Signature Field: If a PDF is marked as having a signature, you can iterate through form fields to find those of /FT /Sig type. With pypdf (PyPDF2), you might do: reader = PdfReader("file.pdf"); fields = reader.get_fields() and then check for any field where field.get("/FT") == "/Sig". Each form field has a /Rect (bounding box) and a page reference. This tells you which page has the digital signature box and its position. If using PyMuPDF, you can iterate through doc.pages and look for widget annotations:
for page in doc:
    for widget in page.widgets():
        if widget.field_type == fitz.PDF_WIDGET_TYPE_SIGNATURE:
            print("Signature field on page", page.number, ":", widget.rect)
(The API might differ, but conceptually you check each widget’s type for “signature”).


DocuSign specifics: Many DocuSign-signed PDFs include an annotation stamp or text like “DocuSigned by [Name]”. You can also do a text search for “DocuSign” or look for X.509 certificate metadata. However, relying on the PDF’s form field is more reliable. PyHanko (an open-source PDF signing toolkit) is another option – it can parse and validate digital signatures. If you only need detection, PyHanko might be overkill, but it’s useful if you later want to extract signer info or ensure the signature is valid.


Setup: Both PyMuPDF and pypdf are installable via pip (pip install pymupdf pypdf). They run entirely offline. PyMuPDF is preferred for performance and simplicity when checking signatures and their locations. Pypdf’s advantage is pure Python and well-documented form field handling. In practice, calling these libraries to detect a signature field is very fast (just reading PDF metadata) and can be done for each file as a preliminary step. If a PDF is digitally signed, you may choose not to alter it (to avoid invalidating the signature) – or use OCRmyPDF’s option to skip signed files. At minimum, flag those pages so you know a certified digital signature was present.


Stage 3: Handwritten Signature & Handwritten Content Detection

This stage deals with handwritten marks on the document: actual signature scribbles made by people, and any form fields filled by hand (printed forms with cursive writing or printed text filled in by pen). These are important to detect so we can exclude them from machine-text OCR or route them to specialized processing.
(3.a) Handwritten Signature Detection (Scribbles):
For finding cursive signatures (typically a name signed in script), the best approach is to use a trained object detection model on page images. A highly effective open-source solution is the YOLOv8-based signature detector released on Hugging Face by Tech4Humans. It’s a fine-tuned YOLOv8 small model that identifies handwritten signature regions in documents. This model was trained on thousands of annotated signatures, making it accurate and fast. Key points:


Model & Usage: The model (yolov8s-signature-detector) can be downloaded from HuggingFace in PyTorch or ONNX format. You can load it with the Ultralytics yolo package or OpenCV’s DNN module. For example, using Ultralytics API:
from ultralytics import YOLO
model = YOLO("yolov8s-signature.pt")  # load the model weights
results = model.predict("page_image.png")
for r in results: 
    boxes = r.boxes.xyxy  # signature bounding boxes
Each detected box comes with a confidence score and class (here just one class “signature”). The model will output coordinates around any scribbles that look like signatures. You can then take those coordinates (relative to the page) and use them to mask out or highlight the signature region.


Example: Below is an example output (visualized) of a YOLOv8 signature detector identifying handwritten signatures on form images. Each colored box is a detected signature, with a confidence score:
 Example of a YOLOv8 model detecting handwritten signatures in document images (each box highlights a signature with a confidence score).
In a Python workflow, you wouldn’t need to draw the boxes (unless for verification); you’d use the coordinates to inform OCR to skip those areas.


Why YOLOv8? YOLO models are well-suited for this task due to their speed and accuracy in object detection. This particular model is open-source (AGPL-3.0) and self-hostable – you run it locally with no internet required. It’s light enough to run on CPU if needed, though a GPU will accelerate it significantly for bulk processing. Using a model trained specifically for signatures drastically improves detection versus trying to DIY with contours or heuristics. The referenced model was benchmarked on various architectures and optimized for a balance of precision and speed.


Setup: Install ultralytics (pip install ultralytics) or download the ONNX and use OpenCV. The ultralytics library makes it easy to run YOLOv8 in Python. Make sure to accept the model’s terms on HuggingFace and download the weights. After that, running inference for each page image in a loop is straightforward.


(3.b) Handwritten Text in Forms:
Beyond signatures, your documents may have other handwritten content (e.g. someone filled out a date, an address, or checkboxes by hand on a printed form). Detecting arbitrary handwritten text vs printed text is a harder problem, but there are tools to help:


OCR-based approach: One practical method is to leverage OCR engines to distinguish handwriting. Tesseract OCR (with appropriate language models) can actually handle handwritten text to some extent, but accuracy is not great. Instead, specialized OCR engines like Kraken (a Python OCR tailored for historical and handwriting recognition) might be used. For example, Kraken or Ocropy can be trained on handwriting and could attempt to read those fields. If you run a handwriting OCR model on the page and it detects text where a standard printed OCR did not, that likely indicates handwritten content. Kraken is open-source and supports modern handwritten text recognition with LSTM models.


Classification approach: If you don’t need to read the handwritten fields, only to flag or exclude them, you can train a simple image classifier to differentiate printed vs handwritten text segments. For instance, use OpenCV to find regions of text (or use Tesseract’s layout analysis to get word bounding boxes), then compute features or a small CNN to classify each region as “printed” or “handwritten.” Handwriting tends to have more variable strokes and connected cursive lines, whereas printed text has more uniform letter shapes. There isn’t an out-of-the-box library that directly gives “handwritten region detection,” but with existing OCR tools you can get candidate text boxes and filter. Another advanced option is LayoutParser with a deep learning layout model – if you fine-tune a detection model to label regions as “handwriting” vs “printed”, LayoutParser can then locate those regions. This requires some ML work, so if you prefer not to train models, a heuristic approach may suffice (e.g. treat any text region that Tesseract failed to decode properly as possibly handwritten).


Recommendation: For a mostly offline pipeline, consider using Tesseract in two passes: one with the standard printed text model and one with a pretrained handwriting model (there are Tesseract models for digits and some handwriting, or use an engine like Calamari). Compare the outputs or confidence. If the page is an image of a form, another trick: printed form text will often be part of the form template (and thus consistent across documents), whereas anything variable is likely handwritten. If you have a blank form template, you could subtract the template image from the filled form image to isolate the ink differences.


In summary, detecting handwriting might combine an object detector for signatures (as above) and an OCR-based detection for any filled text. All the tools mentioned (Ultralytics YOLO, Tesseract, Kraken) are open-source and can be self-hosted. Tesseract and Kraken are installable via pip (pytesseract for Python bindings, kraken via pip as well). These will run on CPU (Kraken can use GPU if available). The output of this stage should be: a list of bounding boxes (or mask regions) covering any content that is not machine-printed text (i.e., signatures, initials, handwritten notes). We’ll use those in Stage 5 to mask out from OCR.
Stage 4: OCR Readability & Page Quality Assessment

Before running OCR on a page, it’s useful to gauge if the page image is clear enough or if it’s a complex case that local OCR might struggle with. This stage flags blurry or low-quality scans and other OCR challenges:


Blur Detection: A common technique to detect blurriness is computing the variance of the Laplacian of the image. Essentially, you apply a Laplace filter (edge detector) and measure the variance; a low variance means not many edges = image is likely blurred. In Python with OpenCV:
import cv2
img = cv2.imread("page.png", cv2.IMREAD_GRAYSCALE)
focus_metric = cv2.Laplacian(img, cv2.CV_64F).var()
if focus_metric < threshold:
    print("Page is blurry")
You’d need to choose a threshold experimentally (e.g. a value like 100 might distinguish sharp vs blurred for 300 DPI scans, but you should adjust based on tests). This is a fast, offline check. There are more advanced blur detectors (e.g. the blur_detector library uses DCT and multi-scale analysis), but for a pipeline the Laplacian method is usually sufficient.


Resolution and Contrast: You should also check if the scan resolution (DPI) is adequate. If using PyMuPDF to render page images, ensure you render at 300 DPI or higher for OCR. If the source PDF has images, you can inspect image dimensions. A page that is, say, 800×1000 pixels for a full A4 sheet is low resolution (about 72 DPI). Such pages might need a fallback because OCR accuracy will drop. Similarly, check for very light or dark scans – you could compute the histogram of pixel intensities to see if the image is very low-contrast. Basic heuristic: if the standard deviation of pixel values is very low (image is nearly all white or all black), the page might be essentially blank or obscured.


Skew/Orientation: A badly skewed scan (text at an angle) or rotated text can affect OCR. OpenCV has methods to detect dominant text angle (e.g. using Hough line transform or projection profile). Consider flagging pages that need de-skew. Tesseract can handle some rotation, but extreme angles will hurt results.


Output: For each page, produce a quality score or boolean flag (e.g. page_blurry=True if below threshold, rotation=15° if detected, etc.). This info can decide if we OCR locally or send to a more robust model (Stage 6). It can also be logged to alert that a rescanning might be needed. All checks in this stage use lightweight operations (OpenCV or even PIL for basic stats) and are easily integrated. They keep the pipeline cheap by avoiding unnecessary OCR attempts on very poor images.


Stage 5: Region Masking to Exclude Headers/Signatures from OCR

Once we have detected which parts of the page are extraneous (headers/footers) or problematic (signatures, handwritten notes), we should exclude those regions when running OCR. This prevents garbage text in OCR output and improves accuracy (since OCR won’t be confused by cursive scribbles or repeated boilerplate text):


Applying Masks: The straightforward way is to mask out the regions on the page image before feeding it to OCR. For example, use PIL or OpenCV to draw solid white rectangles over the bounding boxes identified in Stage 1 (for headers/footers) and Stage 3 (signatures, etc.). If the PDF is text-based (not an image), you might skip image OCR entirely and instead remove those text segments from the extracted text (which you likely did in Stage 1). But for scanned pages, masking on the image is effective. Ensure your mask color blends with background (usually white for paper scans). In code:
import cv2
img = cv2.imread("page.png")
for (x0,y0,x1,y1) in regions_to_mask:
    cv2.rectangle(img, (x0,y0), (x1,y1), (255,255,255), thickness=cv2.FILLED)
cv2.imwrite("masked_page.png", img)
text = pytesseract.image_to_string("masked_page.png", config="--psm 4")
Here regions_to_mask would come from previous detections (coordinates should be in pixel units relative to the image). The pytesseract.image_to_string call runs Tesseract OCR on the cleaned image. We use --psm 4 (assume a single column of text) or another appropriate Page Segmentation Mode depending on layout.


OCR Engine: Tesseract is the go-to OCR engine for offline use. It’s widely regarded as the most accurate free OCR for printed text, and supports dozens of languages. Install Tesseract on your system (e.g. via package manager) and the Python binding pytesseract. Make sure to set the OCR language if not English (e.g. --lang eng for English). Tesseract will output recognized text and can also give coordinates for each word or line if needed (using pytesseract.image_to_data for TSV output). By masking out noisy regions, you avoid Tesseract attempting to interpret scribbles (which would otherwise yield random characters). This is important because Tesseract is known to struggle on handwriting or noisy input – excluding those areas ensures it focuses only on machine-printed text that it excels at.


Performance Consideration: If a page has a lot of content, OCR can be time-consuming. To speed up, you could crop out large blank margins before OCR, or even do two-pass OCR (coarse then refined on certain areas). However, since we aim for scalability, a better approach is running multiple OCR processes in parallel for different pages, if CPU cores allow. The Python multiprocessing module or joblib can help parallelize pytesseract calls page-by-page.


Validation: After OCR, you might quickly scan the text for gibberish or OCR errors. If a page’s OCR result is extremely short or contains many weird characters, that could indicate a problem (and you might choose to route that page to the fallback in Stage 6). Tesseract doesn’t provide a built-in confidence per page, but you can infer quality by the amount of text or presence of many [Il1|0O] confusions etc.


By the end of Stage 5, you will have the main text content of each page (with headers and signatures removed). All of this was done with local tools: OpenCV for masking and Tesseract for OCR – both free and offline. These tools have been integrated in many document-processing systems due to their reliability and cost-effectiveness.
Stage 6: Handling Complex Layouts & Fallback to Vision APIs

Despite the above pipeline, some pages will be too complex for straightforward OCR – e.g. heavily tabular documents, multi-column contracts with dense formatting, very low-quality copies, or pages with diagrams. For such cases, the pipeline should detect the complexity and defer to more powerful (but costly) models like Google’s Gemini Vision or OpenAI’s GPT-4 Vision. These hosted models use advanced AI to interpret visuals and can handle tasks like reading tables or rotated text that standard OCR might mess up.


Complex Layout Detection: You need a trigger to decide when to fallback. Here are some strategies:

Table Detection: If a page contains large tables or forms, consider it complex. You can use open-source table extractors like Camelot to check for tables. Camelot works on PDFs with embedded text – it can parse and return tables if detected. If Camelot finds a big table (e.g. many rows/columns), you might mark that page for special handling. For scanned documents, a deep-learning layout model is useful: LayoutParser has pre-trained models (like PubLayNet) that detect regions of type “Table”, “Text”, “List”, etc. You could run a layout model on the page image; if it flags a large table region or multiple columns, that’s a sign regular OCR might scramble the reading order.
OCR Outcome Heuristic: Alternatively, run Tesseract first and analyze output – if the text is clearly mis-ordered (e.g. columns mixed together) or contains a lot of | or , from table cell boundaries, then the page layout might be beyond simple OCR. Tesseract’s weakness on complex layouts is well documented. So a high ratio of non-alphanumeric characters or very disjoint text could indicate trouble.
Quality Flags: Use results from Stage 4 – if page_blurry or low_contrast is True, you might automatically decide to use a more powerful vision model rather than trust the OCR.


Fallback to Gemini or GPT-4V: For flagged pages, you can call hosted APIs:

Google Gemini Vision API: Google’s new multimodal model (Gemini Pro Vision) can accept image/PDF inputs and return text or analysis. It’s designed to handle large context (up to millions of tokens of text) and understand structured data. Using it typically involves Google’s Vertex AI SDK or REST API. You’d send the page image and ask for a transcription or structured output. This is not open-source, but it’s an optional path when the open-source pipeline hits a limit.
OpenAI GPT-4 Vision: GPT-4 with vision (in the GPT-4V preview) is extremely capable at understanding documents, including complex tables and forms. You could send the image and a prompt like “Extract all text from this image preserving layout” or even ask it to output a JSON of the table. The downside is cost and rate limits, so you’d only use this for pages you absolutely need extra help on (perhaps ones that are illegible to Tesseract or very important to get perfectly).


Integration: Design the pipeline to route pages to these services only if necessary. For example, you might have a rule: if focus_metric < X (very blurry) or detected_table == True or ocr_text_length < Y (maybe OCR failed to read anything significant), then call the API for that page. Ensure you have API keys and error handling set up. Since these are asynchronous calls (network), you might batch them or process them separately from the main flow. Also, be mindful that using an external API on a PDF that was digitally signed could have security/privacy implications – ensure it’s allowed to send out.


Cost Control: The phrase “mostly offline, low-cost” is key – so the idea is to handle 90% of pages with the local pipeline, and only 10% (the hard ones) with paid APIs. Gemini and GPT-4V do incur costs (and possibly require account access). By filtering pages, you minimize those calls. Over time, you can adjust the thresholds if you find too many pages going to fallback. Perhaps you’ll also discover that upgrading your scanning process or using a better OCR (like ABBYY FineReader Engine, if you ever consider a paid on-prem solution) could reduce the need for cloud services.


Both Gemini and GPT-4V are state-of-the-art in 2024–2025 for vision-language tasks. They can interpret formatting, read handwriting more robustly, and even do reasoning (like figure out a table’s structure). So for a truly complex NDA with intricate tables, the fallback might output a cleaner result than Tesseract would. In the context of our pipeline, think of this stage as an “escape hatch” – used sparingly but critical for edge cases.
Integration & Workflow Orchestration

Combining all these stages into a cohesive Python workflow requires careful orchestration. Here are some practical tips for integration:


Pipeline Structure: Organize the processing into a sequence of steps for each document/page. For clarity and maintenance, you might implement each stage as a function (e.g., remove_headers(page), detect_signatures(page), ocr_page(page), etc.). For each PDF, you can do: open it with PyMuPDF or pdfplumber → loop over pages:


Prep Page: Determine if the page has a text layer or if it’s image-only. (Use PyMuPDF: page.get_text("text") returns text if any. If empty, it’s likely a scanned image).


Header/Footer Detection: If you have text layer, remove or mark header/footer text (Stage 1). If image, note the regions to mask later.


Digital Signature Check: If not already done for the whole document, check once per doc for signature fields (Stage 2). If found and tied to a specific page, you might treat that page as special (e.g., you might not OCR a signed signature field – often digital signatures are accompanied by a visual signature or text block which you could skip).


Image Rendering: If OCR is needed (scanned page or you prefer uniform processing), render the page to an image. PyMuPDF’s page.get_pixmap() can rasterize the page at a given DPI. Alternatively, use pdf2image (which uses poppler). Ensure DPI ~ 300 for decent OCR. This image will be used for Stage 3 and 4.


Handwritten Signature & Content Detection: Run the YOLO model on the page image (Stage 3a). Also apply any handwriting detection logic (Stage 3b) – e.g., maybe run a quick OCR with a handwriting model or identify likely handwritten fields. Collect all coordinates of regions that should be ignored in OCR.


Quality Check: Compute blur metric and other quality flags (Stage 4). Decide if the page is too poor for reliable OCR.


Complex Layout Check: (Can be done here or after a first OCR pass.) If you have a layout detection model (like LayoutParser) or use Camelot for tables, apply it now to see if there are large tables or multi-column layouts.


OCR or Fallback: If the page is not flagged as complex and quality is okay, proceed with local OCR:

Mask out header, footer, signature, and handwritten regions on the image (Stage 5).
Run Tesseract OCR on the masked image to extract text.
Store the text result for that page.
If the page is flagged (blurry or complex):
Call the fallback API (Stage 6) with the image (or the original PDF snippet if the API supports PDF directly).
Receive the text/structured result from the API.
Store that result (and note that this page was handled by advanced model).


Compile Output: Merge the text from all pages (taking care to maintain correct order). At this point, you have the full content of the PDF (minus headers/footers). You might output it as a TXT, JSON (with per-page entries), or any format needed for downstream use.


Parallelism: For bulk processing of many documents daily, leverage parallel processing. You can process different PDFs in parallel using threads or processes (be mindful of GIL if using pure Python – I/O heavy parts like PyMuPDF and Tesseract (via subprocess) can be parallelized with threads, but CPU-bound parts like image processing do better with multiprocessing). For example, use Python’s concurrent.futures.ProcessPoolExecutor to OCR pages concurrently. Also, the YOLO model loading is somewhat heavy – you might load it once and reuse it for all pages rather than re-loading per page.


Memory and Cleanup: When processing hundreds of pages, manage resources. Close PDF files after done (doc.close() in PyMuPDF) to free memory. Delete or reuse image objects to avoid bloating RAM. If using OpenCV, large images can eat memory, so consider downscaling a bit if 300 DPI is overkill for certain pages.


Error Handling: Ensure to catch exceptions in each stage. PDFs can be malformed; PyMuPDF might throw errors on some files – catch and log them. Tesseract might fail on some images – handle that gracefully (maybe retry or mark OCR failed). For external API calls, implement retries or skip if the API is down, etc. Logging each step’s outcome (like “page 5: detected signature, skipped OCR on that region”) will help in auditing the pipeline’s performance.


Verification & Tuning: As you integrate, test each stage in isolation on sample docs. For instance, verify that header removal isn’t accidentally cutting out body text (adjust the rules as needed), or that the signature detector isn’t missing signatures (perhaps you might augment it with an additional simple check, like looking for the word “Signature” in the text which often labels where to sign). Tweak thresholds for blur and decide an optimal cutoff that balances not missing slightly blurry but readable pages. This tuning is part of practical deployment.


Finally, remember that all these tools are open-source: PyMuPDF/pdfplumber (PDF parsing), pypdf (forms), YOLO model (AGPL license for signature detection), OpenCV, and Tesseract (Apache 2.0). Combining them leverages their strengths – for example, using computer vision to enhance OCR by removing noise. This multi-stage design keeps costs low (no API calls for the majority of pages) and runs on-premise, and yet is flexible to offload to powerful cloud models for those few pages that truly need it. It’s a balanced approach to processing large volumes of NDAs, contracts, and handwritten documents efficiently each day.
Sources: The recommendations above are based on the capabilities and usage of the mentioned tools as documented in their repositories and community articles. PyMuPDF and pdfplumber usage for header/footer removal is discussed on StackOverflow. Detection of PDF digital signatures via form fields is supported by PyMuPDF’s API. The YOLOv8 signature detection model is detailed in a Hugging Face community article, showing effective identification of handwritten signatures. Tesseract’s strengths and weaknesses (accuracy on printed text vs. complex layouts/handwriting) are noted in an Affinda OCR tools review. OpenCV blur detection using Laplacian variance is a well-known technique used here to flag low-quality scans. By integrating these tools, you create a robust pipeline tailored to your needs without reinventing the wheel, focusing development effort on the orchestration rather than the underlying OCR or vision algorithms.
No results found