Technology
The build is a connected vision and speech system.
VisoraAI uses camera input, OpenCV-style processing, OCR, and text-to-speech. The key is not one model. It is how the layers work together.
Vision capture
The technical flow begins by opening a camera stream and collecting frames. Frame quality is measured before OCR because bad input can make every later step fail.
- Camera frames should be checked for blur, brightness, and possible glare.
- A burst of frames can improve reliability compared with reading one frame.
- Development mode can show visual overlays while user mode should prioritize audio.
Preprocessing
Image processing prepares text for recognition. This includes cropping, scaling, grayscale conversion, denoising, thresholding, and sharpening.
- Different document types may need different preprocessing profiles.
- Adaptive techniques are useful when lighting is uneven.
- Confidence-driven selection can choose the best OCR candidate.
Recognition
The OCR layer converts the enhanced crop into text. It should use confidence filtering so unreliable output is not spoken as if it is correct.
- Tesseract fallback gives the project a practical baseline.
- A CRNN-style model can become a research direction.
- Character Error Rate can compare recognition performance across experiments.
Speech
Text-to-speech makes the result accessible. The spoken output should be paced, chunked, and interruptible.
- Long passages should be split into smaller reading chunks.
- Guidance prompts should not overlap with document reading.
- Future controls can include repeat, pause, and read slower.