Optical character recognition

About: Optical character recognition is a research topic. Over the lifetime, 7342 publications have been published within this topic receiving 158193 citations. The topic is also known as: OCR & optical character reader.

Segmentation of Isolated and Touching Characters in Offline Handwritten Gurmukhi Script Recognition

Abstract: Segmentation of a word into characters is one of the important challenges in optical character recognition. This is even more challenging when we segment characters in an offline handwritten document. Touching characters make this problem more complex. In this paper, we have applied water reservoir based technique for identification and segmentation of touching characters in handwritten Gurmukhi words. Touching characters are segmented based on reservoir base area points. We could achieve 93.51% accuracy for character segmentation with this method. If the characters are neither broken nor overlapping, then this technique shall produce even better results.

Robust language-independent OCR system

Abstract: We present a language-independent optical character recognition system that is capable, in principle, of recognizing printed text from most of the world's languages For each new language or script the system requires sample training data along with ground truth at the text-line level; there is no need to specify the location of either the lines or the words and characters The system uses hidden Markov modeling technology to model each character In addition to language independence, the technology enhances performance for degraded data, such as fax, by using unsupervised adaptation techniques Thus far, we have demonstrated the language-independence of this approach for Arabic, English, and Chinese Recognition results are presented in this paper, including results on faxed data© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering Downloading of the abstract is permitted for personal use only

Hardware architecture of Bidirectional Long Short-Term Memory Neural Network for Optical Character Recognition

Abstract: Optical Character Recognition is conversion of printed or handwritten text images into machine-encoded text. It is a building block of many processes such as machine translation, text-to-speech conversion and text mining. Bidirectional Long Short-Term Memory Neural Networks have shown a superior performance in character recognition with respect to other types of neural networks. In this paper, to the best of our knowledge, we propose the first hardware architecture of Bidirectional Long Short-Term Memory Neural Network with Connectionist Temporal Classification for Optical Character Recognition. Based on the new architecture, we present an FPGA hardware accelerator that achieves 459 times higher throughput than state-of-the-art. Visual recognition is a typical task on mobile platforms that usually use two scenarios either the task runs locally on embedded processor or offloaded to a cloud to be run on high performance machine. We show that computationally intensive visual recognition task benefits from being migrated to our dedicated hardware accelerator and outperforms high-performance CPU in terms of runtime, while consuming less energy than low power systems with negligible loss of recognition accuracy.