99-TODO.ipynb

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    "# Scanned Text Recognition\n",
    "\n",
    "- functionality (mostly done)\n",
    "  - resolution detection, deep language modeling\n",
    "  - font identification, reading order detection\n",
    "  - upsampling, downsampling, better noise removal\n",
    "  - character and word bounding boxes\n",
    "- training / data\n",
    "  - larger, more diverse training sets\n",
    "  - large scale self-supervised training (research)"
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    "# Scanned Text Recognition (more)\n",
    "\n",
    "- other work\n",
    "  - purely convolutional OCR (better suited to current accelerators)\n",
    "  - replace line normalization, layout extraction with deep models\n",
    "  - replace data augmentation with deep models (like GAN)\n",
    "  - better semantic segmentation (text, image, table, graph, figure, ...)\n",
    "  - non-CTC models and/or automatic decoding\n",
    "  - benchmarking of attention-based models"
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    "# Camera Captured Recognition\n",
    "\n",
    "- functionality\n",
    "  - page boundary detection models\n",
    "  - DL dewarping\n",
    "  - DL depth estimation (from RGB, from stereo)\n",
    "- training / data\n",
    "  - large collection of photographically captured images\n",
    "  - automatic generation of photographically distorted images (ray tracing)\n",
    "  - automatic DL-based data augmentation"
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    "# Scene Text Recognition\n",
    "\n",
    "- functionality\n",
    "  - DL text detection / extraction (reimplement standard convolutional models)\n",
    "- training / data\n",
    "  - good datasets exist; train on them\n",
    "  - benchmark CTC vs attention-based models"
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