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Handwritten Text Recognition On Iam

Metrics

CER
WER

Results

Performance results of various models on this benchmark

Model Name
CER
WER
Paper TitleRepository
Decouple Attention Network6.419.6Decoupled Attention Network for Text Recognition-
FPHR+Aug Paragraph Level (~145 dpi)6.3-Full Page Handwriting Recognition via Image to Sequence Extraction-
Transformer w/ CNN7.62-Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition-
Self-Attention + CTC + language model2.75-Rethinking Text Line Recognition Models-
Start, Follow, Read6.423.2Start, Follow, Read: End-to-End Full-Page Handwriting Recognition
HTR-VT(line-level)4.714.9HTR-VT: Handwritten Text Recognition with Vision Transformer-
FPHR Paragraph Level (~145 dpi)6.7-Full Page Handwriting Recognition via Image to Sequence Extraction-
TrOCR-small 62M4.22-TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models-
Leaky LP Cell6.615.9No Padding Please: Efficient Neural Handwriting Recognition-
Transformer + CNN2.96-Rethinking Text Line Recognition Models-
Transformer w/ CNN (+synth)4.67-Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition-
TrOCR-large 558M2.89-TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models-
FPHR+Aug Line Level (~145 dpi)6.5-Full Page Handwriting Recognition via Image to Sequence Extraction-
VAN4.3216.24End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network-
DTrOCR 105M2.38-DTrOCR: Decoder-only Transformer for Optical Character Recognition-
Easter2.06.21-Easter2.0: Improving convolutional models for handwritten text recognition-
LSTM with attention4.87-Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition-
TrOCR-base 334M3.42-TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models-
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