DeepAI AI Chat
Log In Sign Up

StackMix and Blot Augmentations for Handwritten Text Recognition

by   Alex Shonenkov, et al.
Moscow Institute of Physics and Technology

This paper proposes a handwritten text recognition(HTR) system that outperforms current state-of-the-artmethods. The comparison was carried out on three of themost frequently used in HTR task datasets, namely Ben-tham, IAM, and Saint Gall. In addition, the results on tworecently presented datasets, Peter the Greats manuscriptsand HKR Dataset, are provided.The paper describes the architecture of the neural net-work and two ways of increasing the volume of train-ing data: augmentation that simulates strikethrough text(HandWritten Blots) and a new text generation method(StackMix), which proved to be very effective in HTR tasks.StackMix can also be applied to the standalone task of gen-erating handwritten text based on printed text.


page 1

page 2

page 3

page 4


Handwritten text generation and strikethrough characters augmentation

We introduce two data augmentation techniques, which, used with a Resnet...

Preliminary experiments on automatic gender recognition based on online capital letters

In this paper we present some experiments to automatically classify onli...

EASTER: Efficient and Scalable Text Recognizer

Recent progress in deep learning has led to the development of Optical C...

Easter2.0: Improving convolutional models for handwritten text recognition

Convolutional Neural Networks (CNN) have shown promising results for the...

TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text

Handling large corpuses of documents is of significant importance in man...

Handling Heavily Abbreviated Manuscripts: HTR engines vs text normalisation approaches

Although abbreviations are fairly common in handwritten sources, particu...