Handwriting Recognition
Recognizing handwritten text
Handwriting Recognition is a key task in computer vision. Below you will find the standard benchmarks used to evaluate models, along with current state-of-the-art results.
Benchmarks & SOTA
IAM
IAM Handwriting Database
13,353 handwritten text lines from 657 writers. Standard handwriting benchmark.
State of the Art
Start, Follow, Read
23.2
wer
CHURRO-DS
Cultural Heritage Understanding Research Repository OCR Dataset
Historical documents from 46 languages, 99K pages. Tests handwritten and printed text recognition across diverse scripts.
State of the Art
CHURRO (3B)
Stanford
82.3
printed-levenshtein
kohtd
Dataset from Papers With Code
State of the Art
Bluche
8.36
cer
banglalekha-isolated-dataset
Dataset from Papers With Code
State of the Art
AKHCRNet
96.8
accuracy
an-extensive-dataset-of-handwritten-central-kurdis
Dataset from Papers With Code
State of the Art
KHCR
97
1-1-accuracy
Polish EMNIST Extension
EMNIST Extended with Polish Diacritics
Extension of EMNIST dataset with Polish handwritten characters including diacritics (ą, ć, ę, ł, ń, ó, ś, ź, ż). Tests recognition of Polish-specific characters.
No results tracked yet
Related Tasks
General OCR Capabilities
Comprehensive benchmarks covering multiple aspects of OCR performance.
Polish OCR
OCR for Polish language including historical documents, gothic fonts, and diacritic recognition.
Image Classification
Categorizing images into predefined classes (ImageNet, CIFAR).
Object Detection
Locating and classifying objects in images (COCO, Pascal VOC).