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Handwritten Text Recognition On Iam

Metrics

CER
WER

Results

Performance results of various models on this benchmark

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