Use Convolutional Recurrent Neural Network to recognize the Handwritten line text image without pre segmentation into words or characters. Use CTC loss Function to train.

Overview

Handwritten Line Text Recognition using Deep Learning with Tensorflow

Description

Use Convolutional Recurrent Neural Network to recognize the Handwritten line text image without pre segmentation into words or characters. Use CTC loss Function to train. More read this Medium Post

Why Deep Learning?

Why Deep Learning

Deep Learning self extracts features with a deep neural networks and classify itself. Compare to traditional Algorithms it performance increase with Amount of Data.

Basic Intuition on How it Works.

Step_wise_detail

  • First Use Convolutional Recurrent Neural Network to extract the important features from the handwritten line text Image.
  • The output before CNN FC layer (512x100x8) is passed to the BLSTM which is for sequence dependency and time-sequence operations.
  • Then CTC LOSS Alex Graves is used to train the RNN which eliminate the Alignment problem in Handwritten, since handwritten have different alignment of every writers. We just gave the what is written in the image (Ground Truth Text) and BLSTM output, then it calculates loss simply as -log("gtText"); aim to minimize negative maximum likelihood path.
  • Finally CTC finds out the possible paths from the given labels. Loss is given by for (X,Y) pair is: Ctc_Loss
  • Finally CTC Decode is used to decode the output during Prediction.

Detail Project Workflow

Architecture of Model

  • Project consists of Three steps:
    1. Multi-scale feature Extraction --> Convolutional Neural Network 7 Layers
    2. Sequence Labeling (BLSTM-CTC) --> Recurrent Neural Network (2 layers of LSTM) with CTC
    3. Transcription --> Decoding the output of the RNN (CTC decode) DetailModelArchitecture

Requirements

  1. Tensorflow 1.8.0
  2. Flask
  3. Numpy
  4. OpenCv 3
  5. Spell Checker autocorrect >=0.3.0 pip install autocorrect

Dataset Used

  • IAM dataset download from here
  • Only needed the lines images and lines.txt (ASCII).
  • Place the downloaded files inside data directory
The Trained model is available and download from this link. The trained model CER=8.32% and trained on IAM dataset with some additional created dataset.

To Train the model from scratch

$ python main.py --train

To validate the model

$ python main.py --validate

To Prediction

$ python main.py

Run in Web with Flask

$ python upload.py
Validation character error rate of saved model: 8.654728%
Python: 3.6.4 
Tensorflow: 1.8.0
Init with stored values from ../model/snapshot-24
Without Correction clothed leaf by leaf with the dioappoistmest
With Correction clothed leaf by leaf with the dioappoistmest

Prediction output on IAM Test Data PredictionOutput

Prediction output on Self Test Data PredictionOutput

See the project Devnagari Handwritten Word Recognition with Deep Learning for more insights.

Further Improvement

  • Using MDLSTM to recognize whole paragraph at once Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention
  • Line segementation can be added for full paragraph text recognition. For line segmentation you can use A* path planning algorithm or CNN model to seperate paragraph into lines.
  • Better Image preprocessing such as: reduce backgoround noise to handle real time image more accurately.
  • Better Decoding approach to improve accuracy. Some of the CTC Decoder found here

Feel Free to improve this project with pull Request.

This is part of my last semester project of Computer Engineering From Tribhuvan University. July 2019

Owner
sushant097
Machine Learning Engineer | Computer Vision Developer. Working in the field of Research, development of Machine learning and Computer Vision .
sushant097
Table recognition inside douments using neural networks

TableTrainNet A simple project for training and testing table recognition in documents. This project was developed to make a neural network which reco

Giovanni Cavallin 93 Jul 24, 2022
Image Detector and Convertor App created using python's Pillow, OpenCV, cvlib, numpy and streamlit packages.

Image Detector and Convertor App created using python's Pillow, OpenCV, cvlib, numpy and streamlit packages.

Siva Prakash 11 Jan 02, 2022
An OCR evaluation tool

dinglehopper dinglehopper is an OCR evaluation tool and reads ALTO, PAGE and text files. It compares a ground truth (GT) document page with a OCR resu

QURATOR-SPK 40 Dec 20, 2022
Just a script for detecting the lanes in any car game (not just gta 5) with specific resolution and road design ( very basic and limited )

GTA-5-Lane-detection Just a script for detecting the lanes in any car game (not just gta 5) with specific resolution and road design ( very basic and

Danciu Georgian 4 Aug 01, 2021
Page to PAGE Layout Analysis Tool

P2PaLA Page to PAGE Layout Analysis (P2PaLA) is a toolkit for Document Layout Analysis based on Neural Networks. 💥 Try our new DEMO for online baseli

Lorenzo Quirós Díaz 180 Nov 24, 2022
Select range and every time the screen changes, OCR is activated.

ASOCR(Auto Screen OCR) Select range and every time you press Space key, OCR is activated. 範囲を選ぶと、あなたがスペースキーを押すたびに、画面が変わる度にOCRが起動します。 usage1: simple OC

1 Feb 13, 2022
The world's simplest facial recognition api for Python and the command line

Face Recognition You can also read a translated version of this file in Chinese 简体中文版 or in Korean 한국어 or in Japanese 日本語. Recognize and manipulate fa

Adam Geitgey 47k Jan 07, 2023
A version of nrsc5-gui that merges the interface developed by cmnybo with the architecture developed by zefie in order to start a new baseline that is not heavily dependent upon Python processing.

NRSC5-DUI is a graphical interface for nrsc5. It makes it easy to play your favorite FM HD radio stations using an RTL-SDR dongle. It will also displa

61 Dec 22, 2022
PyNeuro is designed to connect NeuroSky's MindWave EEG device to Python and provide Callback functionality to provide data to your application in real time.

PyNeuro PyNeuro is designed to connect NeuroSky's MindWave EEG device to Python and provide Callback functionality to provide data to your application

Zach Wang 45 Dec 30, 2022
Binarize document images

Binarization Binarization for document images Examples Introduction This tool performs document image binarization (i.e. transform colour/grayscale to

QURATOR-SPK 48 Jan 02, 2023
This tool will help you convert your text to handwriting xD

So your teacher asked you to upload written assignments? Hate writing assigments? This tool will help you convert your text to handwriting xD

Saurabh Daware 4.2k Jan 07, 2023
A toolbox of scene text detection and recognition

FudanOCR This toolbox contains the implementations of the following papers: Scene Text Telescope: Text-Focused Scene Image Super-Resolution [Chen et a

FudanVIC Team 170 Dec 26, 2022
Contextual speed detection for python

Speed Prediction using Optical Flow and 2D CNN About the challenge: Comma.AI Speed Challenge This challenge was developed by Comma.AI to predict the s

Mahimana Bhatt 2 Dec 16, 2021
STEFANN: Scene Text Editor using Font Adaptive Neural Network

STEFANN: Scene Text Editor using Font Adaptive Neural Network @ The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.

Prasun Roy 208 Dec 11, 2022
An Implementation of the FOTS: Fast Oriented Text Spotting with a Unified Network

FOTS: Fast Oriented Text Spotting with a Unified Network Introduction This is a pytorch re-implementation of FOTS: Fast Oriented Text Spotting with a

GeorgeJoe 171 Aug 04, 2022
Optical character recognition for Japanese text, with the main focus being Japanese manga

Manga OCR Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses a custom end-to-end model built with Tran

Maciej Budyś 327 Jan 01, 2023
Deskewing images with slanted content

skew_correction De-skewing images with slanted content by finding the deviation using Canny Edge Detection. To Run: In python 3.6, from deskew import

13 Aug 27, 2022
Face Recognizer using Opencv Python

Face Recognizer using Opencv Python The first step create your own dataset with file open-cv-create_dataset second step You can put the photo accordin

Han Izza 2 Nov 16, 2021
Using computer vision method to recognize and calcutate the features of the architecture.

building-feature-recognition In this repository, we accomplished building feature recognition using traditional/dl-assisted computer vision method. Th

4 Aug 11, 2022