Hong Lee

Binary segmentation algorithm for English cursive handwriting recognition

Segmentation in off-line cursive handwriting recognition is a process for extracting individual characters from handwritten words. It is one of the most difficult processes in handwriting recognition because characters are very often connected, slanted and overlapped. Handwritten characters differ in size and shape as well. Hybrid segmentation techniques, especially over-segmentation and validation, are a mainstream to solve the segmentation problem in cursive off-line handwriting recognition. However, the core weakness of the segmentation techniques in the literature is that they impose high risks of chain failure during an ordered validation process. This paper presents a novel Binary Segmentation Algorithm (BSA) that reduces the risks of the chain failure problems during validation and improves the segmentation accuracy. The binary segmentation algorithm is a hybrid segmentation technique and it consists of over-segmentation and validation modules. The main difference between BSA and other techniques in the literature is that BSA adopts an un-ordered segmentation strategy. The proposed algorithm has been evaluated on CEDAR benchmark database and the results of the experiments are very promising.

A novel multiple experts and fusion based segmentation algorithm for cursive handwriting recognition

This paper presents a novel segmentation algorithm for offline cursive handwriting recognition. An over-segmentation algorithm is introduced to dissect the words from handwritten text based on the pixel density between upper and lower baselines. Each segment from the over-segmentation is passed to a multiple expert-based validation process. First expert compares the total foreground pixel of the segmentation point to a threshold value. The threshold is set and calculated before the segmentation by scanning the stroke components in the word. Second expert checks for closed areas such as holes. Third expert validates segmentation points using a neural voting approach which is trained on segmented characters before validation process starts. Final expert is based on oversized segment analysis to detect possible missed segmentation points. The proposed algorithm has been implemented and the experiments on cursive handwritten text have been conducted. The results of the experiments are very promising and the overall performance of the algorithm is more effective than the other existing segmentation algorithms.

Segment confidence-based binary segmentation (SCBS) for cursive handwritten words

A novel segment confidence-based binary segmentation (SCBS) for cursive handwritten words is presented in this paper. SCBS is a character segmentation strategy for off-line cursive handwriting recognition. Unlike the approaches in the literature, SCBS is an unordered segmentation approach. SCBS is repetition of binary segmentation and fusion of segment confidence. Each repetition generates only one final segmentation point. The binary segmentation module is a contour tracing algorithm to find a segmentation path to divide a segment into two segments. A set of segments before binary segmentation is called pre-segments, and a set of segments after binary segmentation is called post-segments. SCBS uses over-segmentation technique to generate suspicious segmentation points on pre-segments. On each suspicious segmentation point, binary segmentation is performed and the highest fusion value is recorded. If the highest fusion value is greater than the one of pre-segments, the suspicious segmentation point becomes the final segmentation point for the iteration. If not, no more segmentation is required. Segment confidence is obtained by fusing mean character, lexical and shape confidences. The proposed approach has been evaluated on local and benchmark (CEDAR) databases.

Binary Segmentation with Neural Validation for Cursive Handwriting Recognition

Over-Segmentation and Validation (OSV) is a well anticipated segmentation strategy in cursive off-line handwriting recognition. Over-Segmentation is a means of locating all possible character boundaries, and the excessive segmentation points called over-segmentation points. Validation is a process to check and validate the segmentation points whether or not they are correct character boundaries by commonly employing an intelligent classifier trained with knowledge of characters. The existing OSV algorithms use ordered validation which means that the incorrect segmentation points might account for the validity of the next segmentation point. The ordered validation creates problems such as chain-failure. This paper presents a novel Binary Segmentation with Neural Validation (BSNV) to reduce the chain-failure. BSNV contains modules of over-segmentation and validation but the main distinctive feature of BSNV is an un-ordered segmentation strategy. The proposed algorithm has been evaluated on CEDAR benchmark database and the results of the experiments are promising.

A neural networks-based fitting to high energy stopping power data for heavy ions in solid matter

Neural networks provide an alternative approach for the solution of complex non-linear data fitting problems. In this paper, we propose a novel technique using a multilayer perceptron neural network to fit high energy stopping power data, where the unknown stopping power functional form was fitted to experimental data by a set of linear combination of neurons. The projectiles of Li, B, N, O, Ne and P in the solid matters C, Si, Ti and Ni are illustrated as examples of the application. Using the resilient backpropagation algorithm, it can obtain more accurate fitting coefficients than conventional iterative methods. Our simulations show that a simple, accurate predictor based on neural network fitting can produce reliable predictions of stopping power values either at the energy position or for the projectile-target combination where no measured data currently exist.

Over-segmentation and Neural Binary Validation for cursive handwriting recognition

A novel Over-Segmentation and Neural Binary Validation (OSNBV) is presented in this paper. OSNBV is a character segmentation strategy for off-line cursive handwriting recognition. Unlike the approaches in the literature, OSNBV is a prioritized segmentation approach. Initially, OSNBV over-segments a handwritten word into primitives. Neural binary validation is iteratively applied to the primitives. The outcome of each iteration is to join two neighboring primitives when the joined one improves the global neural competency. OSNBV introduces Transition Count (TC) and TC for English (EngTC) to prevent under-segmentation error during neural binary validation. OSNBV also incorporates Transition Count Matrix (TCM) into neural global competency. The proposed approach has been evaluated on CEDAR benchmark database. The results showed a significant improvement in segmentation errors. The analysis of results showed that the inclusion of TCM into the validation function has played a major role in improving over-segmentation and bad-segmentation errors.

A Segmentatilon based Adaptive Approach for Curs'ive Handwriltten Text Recognition

The paper presents a segmentation based adaptive approach for the learning and recognition of single persons handwritten text. The approach is incorporated into an automated intelligent system for scanning of handwritten text on a paper and converting it into a text file. It scans an A4 size handwritten page and segments it into lines, words and characters. The segmented characters are passed to a neural classifier for the recognition. The final word is passed through a lexicon based matching process to improve the accuracy of the recognized text. Two neural networks are investigated for the learning of segmented characters quickly and accurately. The experimental results show that the proposed approach can produce high text recognition accuracy with a small number of training samples.

An Automatic Intelligent Language Classifier

The paper presents a novel sentence-based language classifier that accepts a sentence as input and produces a confidence value for each target language. The proposed classifier incorporates Unicode based features and a neural network. The three features Unicode, exclusive Unicode and word matching score are extracted and fed to a neural network for obtaining a final confidence value. The word matching score is calculated by matching words in an input sentence against a common word list for each target language. In a common word list, the most frequently used words for each language are statistically collected and a database is created. The preliminary experiments were performed using test samples from web documents for languages such as English, German, Polish, French, Spanish, Chinese, Japanese and Korean. The classification accuracy of 98.88% has been achieved on a small database.