Handwriting Recognition
50 papers with code • 3 benchmarks • 20 datasets
Benchmarks
These leaderboards are used to track progress in Handwriting Recognition
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Libraries
Use these libraries to find Handwriting Recognition models and implementations
IAM
DeepWriting
RIMES
HKR
Bentham
Ricordi
Konzil
Schiller
Latest papers
LILA-BOTI : Leveraging Isolated Letter Accumulations By Ordering Teacher Insights for Bangla Handwriting Recognition
apurba-nsu-rnd-lab/lila-boti
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Word-level handwritten optical character recognition (OCR) remains a challenge for morphologically rich languages like Bangla.
Star Temporal Classification: Sequence Classification with Partially Labeled Data
facebookresearch/gtn_applications
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These experiments show that STC can recover most of the performance of supervised baseline when up to 70% of the labels are missing.
AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks
This work proposes an attention-based sequence-to-sequence model for handwritten word recognition and explores transfer learning for data-efficient training of HTR systems.
Continuous Offline Handwriting Recognition using Deep Learning Models
For the design of this new model, an extensive analysis of the capabilities of different convolutional architectures in the simplified problem of isolated character recognition has been carried out in order to identify the most suitable ones to be integrated into the continuous model.
KOHTD: Kazakh Offline Handwritten Text Dataset
In this regard, there is a need to implement Handwritten Text Recognition (HTR) which is an automatic way to decrypt records using a computer.
Vietnamese end-to-end speech recognition using wav2vec 2.0
vietai/ASR
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https://github.com/vietai/ASR 2021
Our models are pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of VLSP ASR dataset on 16kHz sampled speech audio.
Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition
Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the non-annotated data.
Handwriting Recognition with Novelty
This paper introduces an agent-centric approach to handle novelty in the visual recognition domain of handwriting recognition (HWR).
Digital Peter: Dataset, Competition and Handwriting Recognition Methods
sberbank-ai/digital_peter_aij2020
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This paper presents a new dataset of Peter the Great's manuscripts and describes a segmentation procedure that converts initial images of documents into the lines.
Full Page Handwriting Recognition via Image to Sequence Extraction
kingyiusuen/image-to-latex
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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.
