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6 articles on Deep Learning.
Before OCR can read text, it must understand page structure. Document layout analysis detects regions, determines reading order, and separates text from tables and figures.
Tables encode structured information that standard OCR misses. Extracting tabular data from scanned documents requires detecting table boundaries, recognizing row and column structure, and mapping cells to their correct positions.
How LSTM networks transformed sequence modeling in handwriting recognition, enabling strong performance on cursive and continuous text.
OCR and HTR serve different purposes: OCR is designed for printed text, while HTR specializes in handwritten documents using sequence-to-sequence models. This guide also explains how to choose software for handwriting-to-text workflows.
Learn essential strategies for training robust OCR models, from dataset construction to hyperparameter optimization and production deployment.
Vision Transformers bring self-attention mechanisms to OCR, enabling parallel processing and strong performance on complex document layouts.