Handwritten recognition (HWR) is the ability of a computer to receive and interpret intelligible handwritten input from source such as paper documents, photographs, touchscreens and other devices. In this paper we will using three (3) classification to recognize the handwritten which is SVM, KNN and Neural Network. Keywords—Handwritten recognition; SVM; K-Nearest Neighbor; Neural Network;

Spelling errors in digital documents are often caused by operational and cognitive mistakes, or by the lack of full knowledge about the language of the written documents. Computer-assisted solutions can help to detect and suggest replacements. In this paper, we present a new string distance metric for the Persian language to rank respelling suggestions of a misspelled Persian word by considerin...

Massive amount of new information being created and the world’s data doubles every 18 months, 80-90% of all data is held in various unstructured formats. Useful information can be derived from this unstructured data. The aim of this research is to present a framework for handling handwritten documents in all its trends. Since the handwritten documents are unstructured data, so the objectives of...

This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotations of any kind in these documents. The handwritten annotations can appear in form of underlines and text by using various writing instruments, e.g., the use of pencils makes the data more challenging. We train and eval...

This paper presents the results of Persian handwritten word recognition based on Mixture of Experts technique. In the basic form of ME the problem space is automatically divided into several subspaces for the experts, and the outputs of experts are combined by a gating network. In our proposed model, we used Mixture of Experts Multi Layered Perceptrons with Momentum term, in the classification ...

In this paper, we present the creation of the first comprehensive database for research and development on handwritten recognition of Dari language. This new handwritten database consists of many aspects of Dari scripts such as: handwritten isolated characters, isolated digits, numeral strings of various lengths, many words/terms, dates, and some special symbols. For each handwritten image in t...

A segmentation algorithm, which can detect different regions of a handwritten document such as text lines, tables and sketches will be extremely useful in a variety of applications such as retrieval, translation and genre classification. However, this task is extremely challenging for handwritten documents, which vary considerably in their structure and content. In this paper, we describe a rob...

This paper develops word recognition methods for historical handwritten cursive and printed documents. It employs a powerful segmentation-free letter detection method based upon joint boosting with histogram-of-gradients features. Efficient inference on an ensemble of hidden Markov models can select the most probable sequence of candidate character detections to recognize complete words in ambi...

Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. In this paper, an efficient interactivepredictive transcription prototype called GIDOC (Gimp-based Interactive transcription of old text DOCuments) is presented. GIDOC is a first attempt to provide integrated support for interactive-predictive page layout analysis, text line detectio...