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About this AI module
This Image-to-Text module is part of the small set of practical tools I maintain for simple media and content work. It extracts readable text from screenshots and documents for faster verification, note-taking, and content preparation.
I use it when image files need to be turned into searchable text for review, documentation, or simple processing tasks. It helps reduce manual typing and makes text checking easier.
It is especially useful for screenshots, receipts, forms, and other image-based records.
Who should use this module?
- People who need to extract text from screenshots and documents.
- Teams reviewing receipts, forms, or proof images.
- Editors and support teams working with image-based records.
How My Image-to-Text Module Works in a Rule-Based OCR Workflow
My current Image-to-Text workflow is built around a local OCR pipeline rather than a general upload-and-read service. In the repo under D:\hustmedia\python, the working path uses local Tesseract OCR, while PaddleOCR and EasyOCR only appear as external service references, not as full OCR logic in this codebase.
The flow starts with a Selenium script that opens the target chat interface and saves a screenshot as screenshot.png. A second script then processes that image with pytesseract, using the fixed Tesseract binary at D:\hustmedia\application\Tesseract-OCR\tesseract.exe. Before OCR runs, the image is cropped to the expected chat area, then refined by detecting a gray edge region to isolate the relevant message area.
The pipeline detects candidate text boxes using contour detection on an Otsu-thresholded image. The boxes are merged by row and horizontal spacing, then filtered with rules for gray regions, uniform backgrounds, and a LINE_THRESHOLD step that removes noisy rows. Instead of reading the whole image, the script keeps only the lowest valid box, expands it with PAD = 5, and runs OCR on that region with pytesseract.image_to_string(..., --psm 7). The extracted text and coordinates are then written to center.json.
This means the current module is not a broad OCR engine for all image types. It is a rule-based OCR workflow designed for a specific chat-style UI, where the goal is to capture the final relevant text line rather than read the full screenshot. That makes it practical for controlled verification tasks, but also dependent on layout consistency.
After OCR, the workflow reads center.json, applies a computer-vision check for a red heart icon, and when needed, sends the extracted text into a lightweight classification step before writing the final check_content result back to JSON. This gives the module both an extraction layer and a validation layer.
At the current stage, the main Flask AI server does not expose a direct public /ocr or /image2text endpoint. So this module should be understood as a working internal OCR component with specific UI-oriented logic, not yet as a universal OCR API.
- OCR stack: local Tesseract OCR
- Python wrapper: pytesseract
- Tesseract binary: D:\hustmedia\application\Tesseract-OCR\tesseract.exe
- Screenshot source: Selenium capture to screenshot.png
- Region output: chat_region.png
- OCR target: lowest valid filtered text box
- Threshold method: Otsu
- OCR mode: --psm 7
- Padding value: PAD = 5
- Output file: center.json
- Extra validation: CV rule check + content classification
- Current limitation: no direct public OCR endpoint
Image Text Extraction
Best for screenshots, receipts, forms, and simple documents. Please upload only content you are authorized to use.
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