Intelligent word recognition

About: Intelligent word recognition is a research topic. Over the lifetime, 2480 publications have been published within this topic receiving 45813 citations.

CNN Based Transfer Learning for Historical Chinese Character Recognition

Abstract: Historical Chinese character recognition has been suffering from the problem of lacking sufficient labeled training samples. A transfer learning method based on Convolutional Neural Network (CNN) for historical Chinese character recognition is proposed in this paper. A CNN model L is trained by printed Chinese character samples in the source domain. The network structure and weights of model L are used to initialize another CNN model T, which is regarded as the feature extractor and classifier in the target domain. The model T is then fine-tuned by a few labeled historical or handwritten Chinese character samples, and used for final evaluation in the target domain. Several experiments regarding essential factors of the CNNbased transfer learning method are conducted, showing that the proposed method is effective.

Robust and highly customizable recognition of online handwritten Japanese characters

Abstract: This paper describes a new online handwritten character recognition system which is composed of coarse classification, linear-time elastic matching, structured character pattern representation and context post-processing. It has marked 90 to 95% correct recognition rates without learning to a large database of on-line handwritten Japanese text. The recognition time is about 0.3 sec./input character on an i486 DX2/66 MHz processor. The system is not only robust to pattern distortions but also highly customizable for personal use. Upon the request of learning an input pattern, it investigates which subpattern (radical) or the pattern as a whole is non-standard, registers the (sub)pattern and extends the effect of the registration to all the character categories whose shapes include it.

Regional context maximum likelihood error correction for OCR, keyboard, and the like

Abstract: A data processing system is disclosed for selecting the correct form of a garbled input word misread by an optical character reader so as to change the number of characters in the word by character splitting or concatenation. Dictionary words are stored in the system, having characters which are flagged for segmentation or concatenation OCR misread propensity. The OCR word and a dictionary word are loaded into a pair of associated shift registers, aligning their letters on one end. The dictionary word characters are inspected for error propensity flags. When a splitting propensity, for example, is found for a character, special conductional probability values are accessed from a storage and a calculation is performed of the probability that the first character of the dictionary word was split by the OCR into the first and second characters of the OCR word. This regional context probability is compared with the probability of a simple substitution error for the characters. If the probability of segmentation is larger, the OCR characters in the first shift register are shifted one space with respect to the dictionary word characters in the second shift register so that subsequent character pairs to be compared are properly matched. The greater calculated probability is combined in a running product. The dictionary word with the largest running product is output by the system as the most likely correct form of the garbled OCR input word. In addition to optical character recognition, the system disclosed may be applied to correcting segmentation errors in phoneme-characters output from a speech analyzer. In addition to optical character recognition, the system disclosed may be applied to correcting character substitutions, transpositions, additions, and omissions inadvertently typed on a keyboard.

Arabic handwritten characters recognition using Deep Belief Neural Networks

Abstract: In the handwriting recognition field, the deep learning is becoming the new trend thanks to their ability to deal with unlabeled raw data especially with the huge size of raw data available nowadays. In this paper, we investigate Deep Belief Neural Network (DBNN) for Arabic handwritten character/word recognition. The proposed system takes the raw data as input and proceeds with a grasping layer-wise unsupervised learning algorithm. The approach was tested on two different databases. For the character level one, the results were promising with an error classification rate of 2.1% on the HACDB database. Unlike, the character level, the evaluation on the ADAB database to deal with word level shows an error rate which exceeds the 40%. Hence, the proposed DBNN structure is not already able to deal with high-level dimensional data and thus has to be improved.

Optical character recognition using artificial neural network

Abstract: Optical character recognition refers to the process of translat ing images of hand-written, typewritten, or printed text into a format understood by machines for the purpose of editing, indexing/searching, and a reduction in storage size. Optical character recognition is the mechanical or electronic translation of images of handwritten, typewritten or printed text into machine-editable text. Artificial neural networks are commonly used to perform character recognition due to their high noise tolerance. In this paper, an Optical character recognition based on Artificial Neural Networks (ANNs). The ANN is trained using the Back Propagation algorithm.