Line segmentation algorithm for Google Vision API.

License: Apache License 2.0

JavaScript 43.37% Kotlin 56.63%

google-vision proposed-algorithm data-extraction invoice segmentation

line-segmentation-algorithm-to-gcp-vision's Introduction

Introduction

Google Vision provides 2 options for optical character recognition(OCR).

- Option 1: TEXT_DETECTION - Words with coordinates
- Option 2: DOCUMENT_TEXT_DETECTION - OCR on dense text to extract lines and paragraph information

The second option is suitable for data extraction from articles (Dense Text such as News Papers/Books). This option has an intelligent segmentation method to merge words which are nearby and form lines and paragraphs.

This feature is not desirable for images with sparse text content such as retail invoices, where the data relevant to the same line resides in two corners (A huge gap/whitespace between the product name and price). For these images the OCR segments the lines in a different order. If the distance of two words in a single line is too far apart then google vision identifies them as two separate paragraphs/lines.

The below images shows the sample output for a typical invoice from google vision.

This behaviour creates a problem in information extraction scenarios. For example, to extract a price of a product from a retail invoice the system needs to find a way to match the words in the same line. The algorithm proposed below performs line segmentation based on characters polygon coordinates for data extraction.

Usage Guide

Usage instruction for each programing language is located in the ReadMe files inside the relevant folders.

Proposed Algorithm

The implemented algorithm runs in two stages

Stage 1 - Groups nearby words to generate a longer strip of line
Stage 2 - Connects words which are far apart using the bounding polygon approach

Explanation.

Stage one helps to reduce the computations needed for the second phase of the algorithm. In the first phase the algorithms tries to merge words/characters which are very near. Stage 1 should be completed because for price related text like $3.40 is presented as 2 words by Google Vision (word 1: $3. word 2:,40). The first stage helps to concat nearby characters to form a text-block/word. This step helps reduces the computation needed for the second phase.

The stage 2 algorithm draws an imaginary bounding polygon (with a threshold) over the words and computes the words which belongs to each line.

Issues.

The algorithm successfully works for most of the slanted and slightly crumpled images. But it will fail to highly crumpled or folded images.

Test

Node JS

cd nodejs
npm install
npm test

Future Work

Try to implement the water-flow algorithm for line segmentation and measure accuracies with bounding polygon approach.

line-segmentation-algorithm-to-gcp-vision's People

Contributors

Stargazers

Watchers

line-segmentation-algorithm-to-gcp-vision's Issues

Use Github Actions for build process

Parts of the response missing

Hi Nirojan,

great work I like it.
Just out of curiosity when I run algorithm on my response some part of the response is missing after the algorithm is completed.

Is this an expected behavior?

Best regards
Sascha

Implement line segmentation algorithm in C#

Hi.

I've ported the project to C# as a .NET standard library.
Would be happy to see this as part of your repo or at least as a reference.

Link to repo:
https://github.com/Inspirare-LLC/Line-Segmentation-Algorithm-To-GCP-Vision

Thanks.

output visualize

I'm implementing end to end line segmentation oct including wrapper for cloud Vision API.

Like the image attached, after you run this program, save it to image (with spatial nearby bounding box and line bounding box) with new bounding box.

Implement line segmentation algorithm for Python

Unresolved function or method getRectangle()

Hi, thanks for sharing your code.

I get an error at line 54 of coordinatesHelper.js with getRectangle that is not defined.

Do you have an idea how to solve that?

TIA

Integrating w/ PHP

Hi Niro,

I have a PHP based server app which is using the GCV PHP Library. I am able to run GCV perfectly, but badly need to integrate this package. My solution has been to setup a NodeJS Express microservice w/ api and connect my PHP app to that microservice. That worked perfectly however I'm struggling getting the package to accept my GCV results.

I feel like I am SO close, but would love some help crossing the finish line if you're up for it!

My PHP code to send the response to the NodeJS microservice:

$imageAnnotator = new ImageAnnotatorClient();

$gcv_response = $imageAnnotator->documentTextDetection($preprocessed_image_file_contents);
        
$json_gsv_response = json_encode($gcv_response->serializeToJsonString());
        
try{
    $response = Http::post('http://localhost:3000/line-segmentation',[
        'gcv_response' => $json_gsv_response
    ]);
}
catch(\Exception $exc)
{
    Log::info('$exc: ');
    Log::info(print_r($exc->getMessage(), true));
}

My JS inside the NodeJS microservice:

const express = require('express');

const LineSegmentationAlgorithmToGCPVision = require('line-segmentation-algorithm-to-gcp-vision');

const app = express();
const PORT = process.env.PORT || 3000;

app.use(express.json());

app.post('/line-segmentation', async (req, res) => {
	try {
		const gcv_response = req.body['gcv_response'];

		const parsed_gcv_response = JSON.parse(gcv_response);

		var result =
			LineSegmentationAlgorithmToGCPVision.initLineSegmentation(
				parsed_gcv_response
			);

		res.json({ result });
	} catch (error) {
		res.status(500).json({ error: error.message });
	}
});

app.listen(PORT, () => {
	console.log(`Node.js microservice listening at http://localhost:${PORT}`);
});

The error I'm getting inside of my PHP:

[2023-03-28 13:55:22] development.INFO: HTTP request returned status code 500:
{"error":"Cannot read properties of undefined (reading '0')"}

When I echo out the parsed_gcv_response variable from the NodeJS microservice I am getting this object:

{"textAnnotations":[{"locale":"en","description":"1 Mojito del King\n1 Cilantro Smash\n1 Guac + Chips\n2 Baja 2.0\n1 SPICY MARGARITA\n1 FUT XX\n1 TO Churros con Nutella\n1\nCamarones y Queso\n$12.95\n$11.95\n$8.95\n$21.90\n$12.95\n$9.95\n$5.95\n$12.90\n$97.50\nSOUS-TOTAL\nTPS\n$4.88\nTVQ\n$9.73\nTOTAL $112.11\n700 4900F0F000 CA","boundingPoly":{"vertices":[{"y":16},{"x":981,"y":16},{"x":981,"y":1015},{"y":1015}]}},{"description":"1","boundingPoly":{"vertices":[{"x":7,"y":17},{"x":27,"y":17},{"x":26,"y":74},{"x":6,"y":74}]}},{"description":"Mojito","boundingPoly":{"vertices":[{"x":57,"y":17},{"x":219,"y":20},{"x":218,"y":78},{"x":56,"y":75}]}},{"description":"del","boundingPoly":{"vertices":[{"x":248,"y":20},{"x":324,"y":21},{"x":323,"y":79},{"x":247,"y":78}]}},{"description":"King","boundingPoly":{"vertices":[{"x":354,"y":22},{"x":465,"y":24},{"x":464,"y":81},{"x":353,"y":79}]}},{"description":"1","boundingPoly":{"vertices":[{"x":9,"y":84},{"x":29,"y":84},{"x":28,"y":133},{"x":8,"y":133}]}},{"description":"Cilantro","boundingPoly":{"vertices":[{"x":55,"y":84},{"x":271,"y":87},{"x":270,"y":136},{"x":54,"y":133}]}},{"description":"Smash","boundingPoly":{"vertices":[{"x":300,"y":87},{"x":434,"y":89},{"x":433,"y":138},{"x":299,"y":136}]}},{"description":"1","boundingPoly":{"vertices":[{"x":9,"y":148},{"x":30,"y":148},{"x":29,"y":199},{"x":8,"y":199}]}},{"description":"Guac","boundingPoly":{"vertices":[{"x":58,"y":148},{"x":162,"y":150},{"x":161,"y":202},{"x":57,"y":200}]}},{"description":"+","boundingPoly":{"vertices":[{"x":193,"y":152},{"x":218,"y":153},{"x":217,"y":204},{"x":192,"y":203}]}},{"description":"Chips","boundingPoly":{"vertices":[{"x":246,"y":153},{"x":376,"y":156},{"x":375,"y":207},{"x":245,"y":204}]}},{"description":"2","boundingPoly":{"vertices":[{"x":2,"y":220},{"x":30,"y":220},{"x":30,"y":269},{"x":2,"y":269}]}},{"description":"Baja","boundingPoly":{"vertices":[{"x":54,"y":220},{"x":164,"y":221},{"x":164,"y":270},{"x":54,"y":269}]}},{"description":"2.0","boundingPoly":{"vertices":[{"x":189,"y":220},{"x":273,"y":220},{"x":273,"y":270},{"x":189,"y":270}]}},{"description":"1","boundingPoly":{"vertices":[{"x":7,"y":283},{"x":27,"y":283},{"x":27,"y":331},{"x":7,"y":331}]}},{"description":"SPICY","boundingPoly":{"vertices":[{"x":58,"y":283},{"x":191,"y":284},{"x":190,"y":332},{"x":58,"y":331}]}},{"description":"MARGARITA","boundingPoly":{"vertices":[{"x":218,"y":284},{"x":458,"y":286},{"x":457,"y":335},{"x":217,"y":333}]}},{"description":"1","boundingPoly":{"vertices":[{"x":7,"y":351},{"x":26,"y":351},{"x":25,"y":395},{"x":6,"y":395}]}},{"description":"FUT","boundingPoly":{"vertices":[{"x":57,"y":351},{"x":138,"y":352},{"x":137,"y":396},{"x":56,"y":395}]}},{"description":"XX","boundingPoly":{"vertices":[{"x":159,"y":352},{"x":217,"y":353},{"x":216,"y":398},{"x":158,"y":397}]}},{"description":"1","boundingPoly":{"vertices":[{"x":6,"y":418},{"x":24,"y":418},{"x":24,"y":466},{"x":6,"y":466}]}},{"description":"TO","boundingPoly":{"vertices":[{"x":55,"y":418},{"x":106,"y":418},{"x":106,"y":466},{"x":55,"y":466}]}},{"description":"Churros","boundingPoly":{"vertices":[{"x":136,"y":418},{"x":322,"y":418},{"x":322,"y":466},{"x":136,"y":466}]}},{"description":"con","boundingPoly":{"vertices":[{"x":349,"y":418},{"x":427,"y":418},{"x":427,"y":466},{"x":349,"y":466}]}},{"description":"Nutella","boundingPoly":{"vertices":[{"x":459,"y":418},{"x":644,"y":418},{"x":644,"y":466},{"x":459,"y":466}]}},{"description":"1","boundingPoly":{"vertices":[{"x":6,"y":482},{"x":26,"y":482},{"x":25,"y":532},{"x":5,"y":532}]}},{"description":"Camarones","boundingPoly":{"vertices":[{"x":54,"y":482},{"x":296,"y":485},{"x":295,"y":536},{"x":53,"y":533}]}},{"description":"y","boundingPoly":{"vertices":[{"x":323,"y":486},{"x":349,"y":486},{"x":348,"y":536},{"x":322,"y":536}]}},{"description":"Queso","boundingPoly":{"vertices":[{"x":373,"y":486},{"x":512,"y":488},{"x":511,"y":539},{"x":372,"y":537}]}},{"description":"$","boundingPoly":{"vertices":[{"x":808,"y":25},{"x":838,"y":24},{"x":840,"y":71},{"x":810,"y":72}]}},{"description":"12.95","boundingPoly":{"vertices":[{"x":838,"y":23},{"x":973,"y":18},{"x":975,"y":66},{"x":840,"y":71}]}},{"description":"$","boundingPoly":{"vertices":[{"x":810,"y":89},{"x":839,"y":89},{"x":840,"y":135},{"x":811,"y":135}]}},{"description":"11.95","boundingPoly":{"vertices":[{"x":838,"y":88},{"x":972,"y":86},{"x":973,"y":132},{"x":839,"y":134}]}},{"description":"$","boundingPoly":{"vertices":[{"x":838,"y":157},{"x":863,"y":157},{"x":863,"y":200},{"x":838,"y":200}]}},{"description":"8.95","boundingPoly":{"vertices":[{"x":864,"y":156},{"x":970,"y":155},{"x":970,"y":199},{"x":864,"y":200}]}},{"description":"$","boundingPoly":{"vertices":[{"x":809,"y":222},{"x":837,"y":222},{"x":837,"y":268},{"x":809,"y":268}]}},{"description":"21.90","boundingPoly":{"vertices":[{"x":835,"y":222},{"x":974,"y":222},{"x":974,"y":268},{"x":835,"y":268}]}},{"description":"$","boundingPoly":{"vertices":[{"x":807,"y":289},{"x":836,"y":289},{"x":837,"y":335},{"x":808,"y":335}]}},{"description":"12.95","boundingPoly":{"vertices":[{"x":839,"y":288},{"x":972,"y":286},{"x":973,"y":332},{"x":840,"y":334}]}},{"description":"$","boundingPoly":{"vertices":[{"x":836,"y":358},{"x":862,"y":357},{"x":863,"y":400},{"x":837,"y":401}]}},{"description":"9.95","boundingPo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Implement line segmentation algorithm in node-red flow

Hi.

I've ported the project to a node-red flow.

https://github.com/Yusseiin/node-red-gpc-ocr-line-segmentation

Thanks for your work.

But it didnt do anything. Please help. I'm not able to debug as I'm not familiar with nodsJS.

Regards,
Gautam

// rtl support

if(dir === 'rtl' ){
lines = lines;
rawText = raw;
}
else{
// reverse to use lifo, because array.shift() will consume 0(n)
lines = lines.reverse();
rawText = rawText.reverse();
}

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sshniro / line-segmentation-algorithm-to-gcp-vision Goto Github PK