Lecture notes are important for students to review and understand the key points in the class. Unfortunately, the students often miss or lose part of the lecture notes. In this paper, we design and implement an infrared sensor based system, InfraNotes, to automatically record the notes on the board by sensing and analyzing hand gestures of the lecturer. Compared with existing techniques, our system does not require special accessories with lecturers such as sensor-facilitated pens, writing surfaces or the video-taping infrastructure. Instead, it only has an infrared-sensor module on the eraser holder of black white board to capture handwritten trajectories. With a lightweight framework for handwritten trajectory processing, clear lecture notes can be generated automatically. We evaluate the quality of lecture notes by three standard character recognition techniques. The results indicate that InfraNotes is a promising solution to create clear and complete lectures to promote the education.
Hand writing recognition is the task of transforming a language represented in its spatial form of graphical marks into its symbolic representation. There are two kinds of handwriting input, on-line and off-line @cite_5 . On-line handwriting input maintains the time series of writing points, order of strokes and additional information about pen tip (velocity, acceleration). For example, handwriting input methods on cell phones and tablets receive on-line handwriting input when users touch the screen. Preprocessing of on-line recognition includes noise removal, stroke and character segmentation. Off-line handwriting input only preserves images of the completed on-board writing area. For example, banks recognize handwritten amounts on checks. Preprocessing of off-line recognition includes setting thresholds to extract writing points, removal of noise, segmentation of writing lines, and finally segmentation of characters and words. Our system is a type of on-line handwriting input system and records time stamps of each points on the handwritten trajectory. However, the sensor tracks both on-board and off-board movement of writing tools. Thus, we need classification after segmentation to determine which strokes are belong to on-board writing or off-board hand movement.
@cite_5
2343436474 2167898728 2036889485 2005255079
Offline handwriting recognition systems require cropped text line images for both training and recognition. On the one hand, the annotation of position and transcript at line level is costly to obtain. On the other hand, automatic line segmentation algorithms are prone to errors, compromising the subsequent recognition. In this paper, we propose a modification of the popular and efficient multi-dimensional long short-term memory recurrent neural networks (MDLSTM-RNNs) to enable end-to-end processing of handwritten paragraphs. More particularly, we replace the collapse layer transforming the two-dimensional representation into a sequence of predictions by a recurrent version which can recognize one line at a time. In the proposed model, a neural network performs a kind of implicit line segmentation by computing attention weights on the image representation. The experiments on paragraphs of Rimes and IAM database yield results that are competitive with those of networks trained at line level, and constitute a significant step towards end-to-end transcription of full documents. In online handwriting recognition the trajectory of the pen is recorded during writing. Although the trajectory provides a compact and complete representation of the written output, it is hard to transcribe directly, because each letter is spread over many pen locations. Most recognition systems therefore employ sophisticated preprocessing techniques to put the inputs into a more localised form. However these techniques require considerable human effort, and are specific to particular languages and alphabets. This paper describes a system capable of directly transcribing raw online handwriting data. The system consists of an advanced recurrent neural network with an output layer designed for sequence labelling, combined with a probabilistic language model. In experiments on an unconstrained online database, we record excellent results using either raw or preprocessed data, well outperforming a state-of-the-art HMM based system in both cases. In this paper, an off-line, text independent system for writer identification and verification of handwritten text lines using Hidden Markov Model (HMM) based recognizers is presented. For each writer, an individual recognizer is built and trained on text lines of that writer. This results in a number of recognizers, each of which is an expert on the handwriting of exactly one writer. In the identification and verification phase, a text line of unknown origin is presented to each of these recognizers and each one returns a transcription that includes the log-likelihood score for the generated output. These scores are sorted and the resulting ranking is used for both identification and verification. Several confidence measures are defined on this ranking. The proposed writer identification and verification system is evaluated using different experimental setups. We propose a real-time finger writing character recognition system using depth information. This system allows user to input characters by writing freely in the air with the Kinect. During the writing process, it is reasonable to assume that the finger and hand are always holding in front of torso. Firstly, we compute the depth histogram of human body and use a switch mixture Gaussian model to characterize it. Since the hand is closer to camera, a model-based threshold can segment the hand-related region out. Then, we employ an unsupervised clustering algorithm, K-means, to classify the segmented region into two parts, the finger-hand part and hand-arm part. By identifying the arm direction, we can determine the finger-hand cluster and locate the fingertip as the farthest point from the other cluster. We collected over 8000 frames writing-in-the-air sequences including two different subjects writing numbers, strokes, pattern, English and Chinese characters from two different distances. From our experiments, the proposed algorithm can provide robust and accurate fingertip detection, and achieve encouraging character recognition result.