Towards an IMU-based Pen Online Handwriting Recognizer
Most online handwriting recognition systems require the use of specific writing surfaces to extract positional data. In this paper we present a online handwriting recognition system for word recognition which is based on inertial measurement units (IMUs) for digitizing text written on paper. This is obtained by means of a sensor-equipped pen that provides acceleration, angular velocity, and magnetic forces streamed via Bluetooth. Our model combines convolutional and bidirectional LSTM networks, and is trained with the Connectionist Temporal Classification loss that allows the interpretation of raw sensor data into words without the need of sequence segmentation. We use a dataset of words collected using multiple sensor-enhanced pens and evaluate our model on distinct test sets of seen and unseen words achieving a character error rate of 17.97% and 17.08%, respectively, without the use of a dictionary or language model
Code (0)
등록된 구현이 없습니다.
Tasks
Handwriting RecognitionLanguage ModelingLanguage ModellingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Data Incubation -- Synthesizing Missing Data for Handwriting Recognition
In this paper, we demonstrate how a generative model can be used to build a better recognizer through the control of content and style. We are building an online handwriting recognizer from a modest amount of training sa…
Handwriting RecognitionRepresenting Online Handwriting for Recognition in Large Vision-Language Models
The adoption of tablets with touchscreens and styluses is increasing, and a key feature is converting handwriting to text, enabling search, indexing, and AI assistance. Meanwhile, vision-language models (VLMs) are now th…
Handwriting RecognitionOptical Character RecognitionOptical Character Recognition (OCR)Co-adaptation in a Handwriting Recognition System
Handwriting is a natural and versatile method for human-computer interaction, especially on small mobile devices such as smart phones. However, as handwriting varies significantly from person to person, it is difficult t…
Handwriting RecognitionSLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text
Large amounts of labeled data are urgently required for the training of robust text recognizers. However, collecting handwriting data of diverse styles, along with an immense lexicon, is considerably expensive. Although …
AttributeDiversityGenerative Adversarial NetworkDigitizing Handwriting with a Sensor Pen: A Writer-Independent Recognizer
Online handwriting recognition has been studied for a long time with only few practicable results when writing on normal paper. Previous approaches using sensor-based devices encountered problems that limited the usage o…
Handwriting Recognition