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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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