A unified method for augmented incremental recognition of online handwritten Japanese and English text

We present a unifed method to augmented incremental recognition for online handwritten Japanese and English text, which is used for busy or on-the-fly recognition while writing, and lazy or delayed recognition after writing, without incurring long waiting times. It extends the local context for segmentation and recognition to a range of recent strokes called "segmentation scope" and "recognition scop", respectively. The recognition scope is inside of the segmentation scope. The augmented incremental recognition triggers recognition at every several recent strokes, updates the segmentation and recognition candidate lattice, and searches over the lattice for the best result incrementally. It also incorporates three techniques. The frst is to reuse the segmentation and recognition candidate lattice in the previous recognition scope for the current recognition scope. The second is to fx undecided segmentation points if they are stable between character/word patterns. The third is to skip recognition of partial candidate character/word patterns. The augmented incremental method includes the case of triggering recognition at every new stroke with the above-mentioned techniques. Experiments conducted on TUAT-Kondate and IAM online database show its superiority to batch recognition (recognizing text at one time) and pure incremental recognition (recognizing text at every input stroke) in processing time, waiting time, and recognition accuracy

New Distance Measures for Arabic Handwritten Text Recognition

recent years, optical character recognition has attracted scientists and researchers. Latin, Chinese, Korean and Thai characters have been researched more thoroughly than Arabic characters. The research has concentrated firstly on printed and typeset characters until acceptable recognition accuracy has been achieved. Nowadays, most of the researches have gone towards handwritten character recognition. Arabic text is cursive as characters in a sub-word are connected to each other. This makes the recognition process more complex and a segmentation procedure is required to separate the connected characters from each other before they can be recognized. Features extracted have to be chosen carefully since it has a very important role in the segmentation and recognition process. The recognition accuracy mostly depends on the classifier applied and the segmentation procedure. In this research work, a framework for recognizing the Arabic handwriting is presented. Two approaches have been proposed. The first approach has been designed to recognize the word as a whole to fit applications such as sorting postal mails and bank checks where the number of words or digits that need to be recognized is limited. The words may include country and city names written on postal mails, or some reserved words or amounts used on bank checks. The second approach represents the general case where any type of documents or handwritten text can be recognized by this approach. In both approaches, a preprocessing stage including image enhancement and normalization. The most significant features are extracted by implementing the Principal Components Analysis. A new segmentation-based approach is designed and implemented for the second approach to segment the text into characters, while no or simple segmentation procedure is performed in the first approach. The recognition step is performed by applying the nearest neighbor algorithm. Four different distance measures are used with the nearest neighbor, the first norm, second norm (Euclidean), and two new norms proposed called ENorm, EEuclidean. The two new norms proposed (ENorm, EEuclidean) are derived from the first and second norm respectively. The recognition accuracy is enhanced by using the two new norms proposed. The approaches have been tested as well, and a number of experiments have been discussed more thoroughly. The first approach is experimented by four datasets, which are sub-words containing two characters, sub-words containing three characters, Latin letters and Hindi digits which are used with Arabic language nowadays. The recognition accuracy is the attribute used for measurement, and an 8-fold cross validation technique is used to test this attribute. The average recognition accuracy is 94.8% for the digits, 78% for the three-character sub-words, 77% for the two-character sub-words and 67% for Latin letters. The second approach has achieved recognition accuracy of 73% without detecting dots and 77% with dot detection

WordFences: Text localization and recognition

En col·laboració amb la Universitat de Barcelona (UB) i la Universitat Rovira i Virgili (URV)In recent years, text recognition has achieved remarkable success in recognizing scanned document text. However, word recognition in natural images is still an open problem, which generally requires time consuming post-processing steps. We present a novel architecture for individual word detection in scene images based on semantic segmentation. Our contributions are twofold: the concept of WordFence, which detects border areas surrounding each individual word and a unique pixelwise weighted softmax loss function which penalizes background and emphasizes small text regions. WordFence ensures that each word is detected individually, and the new loss function provides a strong training signal to both text and word border localization. The proposed technique avoids intensive post-processing by combining semantic word segmentation with a voting scheme for merging segmentations of multiple scales, producing an end-to-end word detection system. We achieve superior localization recall on common benchmark datasets - 92% recall on ICDAR11 and ICDAR13 and 63% recall on SVT. Furthermore, end-to-end word recognition achieves state-of-the-art 86% F-Score on ICDAR13