CITlab ARGUS for Arabic Handwriting
In the recent years it turned out that multidimensional recurrent neural networks (MDRNN) perform very well for offline handwriting recognition tasks like the OpenHaRT 2013 evaluation DIR. With suitable writing preprocessing and dictionary lookup, our ARGUS software completed this task with an error rate of 26.27% in its primary setup.
Code (0)
등록된 구현이 없습니다.
Tasks
Handwriting RecognitionSimilar Papers 제목 키워드 기반
CITlab ARGUS for historical data tables
We describe CITlab's recognition system for the ANWRESH-2014 competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises word recognition from segmente…
General ClassificationHandwriting RecognitionCITlab ARGUS for historical handwritten documents
We describe CITlab's recognition system for the HTRtS competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises the recognition of historical handwri…
General ClassificationHandwriting RecognitionCITlab ARGUS for historical handwritten documents
We describe CITlab's recognition system for the HTRtS competition attached to the 13. International Conference on Document Analysis and Recognition, ICDAR 2015. The task comprises the recognition of historical handwritte…
General ClassificationAltecOnDB: A Large-Vocabulary Arabic Online Handwriting Recognition Database
Arabic is a semitic language characterized by a complex and rich morphology. The exceptional degree of ambiguity in the writing system, the rich morphology, and the highly complex word formation process of roots and patt…
Handwriting RecognitionSentenceAlexU-Word: A New Dataset for Isolated-Word Closed-Vocabulary Offline Arabic Handwriting Recognition
In this paper, we introduce the first phase of a new dataset for offline Arabic handwriting recognition. The aim is to collect a very large dataset of isolated Arabic words that covers all letters of the alphabet in all …
AllHandwriting Recognition