HKR For Handwritten Kazakh & Russian Database
원문 초록
In this paper, we present a new Russian and Kazakh database (with about 95% of Russian and 5% of Kazakh words/sentences respectively) for offline handwriting recognition. A few pre-processing and segmentation procedures have been developed together with the database. The database is written in Cyrillic and shares the same 33 characters. Besides these characters, the Kazakh alphabet also contains 9 additional specific characters. This dataset is a collection of forms. The sources of all the forms in the datasets were generated by \LaTeX which subsequently was filled out by persons with their handwriting. The database consists of more than 1400 filled forms. There are approximately 63000 sentences, more than 715699 symbols produced by approximately 200 different writers. It can serve researchers in the field of handwriting recognition tasks by using deep and machine learning.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
DeepFRC: An End-to-End Deep Learning Model for Functional Registration and Classification
DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents
Character Queries: A Transformer-based Approach to On-Line Handwritten Character Segmentation
Writer adaptation for offline text recognition: An exploration of neural network-based methods
FPGA Implementation of Convolutional Neural Network for Real-Time Handwriting Recognition
BN-DRISHTI: Bangla Document Recognition through Instance-level Segmentation of Handwritten Text Images
토론
이 논문과 연결된 포럼 글이 아직 없습니다.