Papers › Full Page Handwriting Recognition via Image to Sequence Extraction
Full Page Handwriting Recognition via Image to Sequence Extraction
Sumeet S. Singh, Sergey Karayev
We present a Neural Network based Handwritten Text Recognition (HTR) model architecture that can be trained to recognize full pages of handwritten or printed text without image segmentation. Being based on Image to Sequence architecture, it can extract text present in an image and then sequence it correctly without imposing any constraints regarding orientation, layout and size of text and non-text. Further, it can also be trained to generate auxiliary markup related to formatting, layout and content. We use character level vocabulary, thereby enabling language and terminology of any subject. The model achieves a new state-of-art in paragraph level recognition on the IAM dataset. When evaluated on scans of real world handwritten free form test answers - beset with curved and slanted lines, drawings, tables, math, chemistry and other symbols - it performs better than all commercially available HTR cloud APIs. It is deployed in production as part of a commercial web application.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Handwritten Text Recognition | IAM | FPHR+Aug Paragraph Level (~145 dpi) | CER | 6.3 | #11 of 17 | Archive leaderboard | report |
| Handwritten Text Recognition | IAM | FPHR+Aug Line Level (~145 dpi) | CER | 6.5 | #14 of 17 | Archive leaderboard | report |
| Handwritten Text Recognition | IAM | FPHR Paragraph Level (~145 dpi) | CER | 6.7 | #16 of 17 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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