@flexabrain/mcp-server
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Advanced electrical schematic analysis MCP server with rail engineering expertise
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JavaScript
/**
* FlexaBrain MCP Server - Enhanced OCR Service
*
* Advanced OCR service specifically designed for complex technical drawings
* and multi-page traction generator monitoring control schematics.
*
* Extends the base OCR service with:
* - Dense layout text extraction
* - Line and connection detection
* - Generator monitoring component patterns
* - Multi-resolution processing
* - Technical drawing feature recognition
*/
import { createWorker, PSM } from 'tesseract.js';
export class EnhancedOCRService {
worker = null;
isInitialized = false;
processingCache = new Map();
// Enhanced patterns for generator monitoring systems
GENERATOR_COMPONENT_PATTERNS = {
GENERATOR_CONTROL: {
component_type: 'generator_control',
patterns: [
/^GEN\d{1,4}[A-Z]?$/i, // GEN001, GEN205A
/^G\d{1,4}[A-Z]?$/i, // G001, G205A
/^GENMON\d+[A-Z]?$/i, // GENMON01
/^GENCTRL\d+[A-Z]?$/i // GENCTRL01
],
prefixes: ['GEN', 'G', 'GENMON', 'GENCTRL'],
context_clues: ['generator', 'monitoring', 'control', 'output', 'excitation'],
confidence_boost: 0.94,
typical_locations: ['control_room', 'generator_bay', 'monitoring_panel'],
associated_parameters: ['voltage', 'frequency', 'power', 'temperature', 'current']
},
MONITORING_SENSOR: {
component_type: 'monitoring_sensor',
patterns: [
/^TE\d{1,4}[A-Z]?$/i, // TE001 (Temperature Element)
/^PE\d{1,4}[A-Z]?$/i, // PE001 (Pressure Element)
/^VE\d{1,4}[A-Z]?$/i, // VE001 (Vibration Element)
/^FE\d{1,4}[A-Z]?$/i, // FE001 (Flow Element)
/^MON\d{1,4}[A-Z]?$/i // MON001 (General Monitor)
],
prefixes: ['TE', 'PE', 'VE', 'FE', 'MON'],
context_clues: ['temperature', 'pressure', 'vibration', 'flow', 'sensor', 'monitor'],
confidence_boost: 0.88,
typical_locations: ['generator', 'bearing', 'cooling_system', 'lubrication'],
associated_parameters: ['range', 'alarm', 'trip', 'units', 'calibration']
},
CONTROL_RELAY: {
component_type: 'control_relay',
patterns: [
/^[A-Z]{1,3}R\d*[A-Z]?$/i, // KR101, TR205A, AR01
/^R\d{1,4}[A-Z]?$/i, // R101, R205A
/^REL\d+[A-Z]?$/i, // REL101A
/^CR\d+[A-Z]?$/i // CR101 (Control Relay)
],
prefixes: ['R', 'KR', 'TR', 'AR', 'REL', 'CR'],
context_clues: ['relay', 'control', 'auxiliary', 'protection', 'interlock'],
confidence_boost: 0.85,
typical_locations: ['control_cabinet', 'relay_panel', 'protection_panel'],
associated_parameters: ['coil_voltage', 'contact_rating', 'pickup', 'dropout']
},
PROTECTION_DEVICE: {
component_type: 'protection_device',
patterns: [
/^[0-9]{2}[A-Z]{1,3}\d*$/i, // 27GEN, 59GEN, 81GEN (IEEE device numbers)
/^[0-9]{2}\/[0-9]{2}[A-Z]*$/i, // 87/87N (Differential protection)
/^PROT\d+[A-Z]?$/i // PROT01
],
prefixes: ['27', '59', '81', '87', 'PROT'],
context_clues: ['protection', 'undervoltage', 'overvoltage', 'frequency', 'differential'],
confidence_boost: 0.92,
typical_locations: ['protection_panel', 'control_room'],
associated_parameters: ['setpoint', 'time_delay', 'pickup', 'reset']
}
};
constructor() {
this.initializeWorker();
}
async initializeWorker() {
try {
console.error('FlexaBrain Enhanced OCR: Initializing Tesseract worker...');
this.worker = await createWorker('eng');
// Configure for technical drawing text recognition
await this.worker.setParameters({
tessedit_char_whitelist: 'ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789.-+_/()',
tessedit_pageseg_mode: PSM.SPARSE_TEXT,
tessedit_ocr_engine_mode: 1, // Neural nets LSTM engine
// Enhanced settings for technical drawings
tessedit_enable_bigram_correction: '1',
tessedit_enable_dict_correction: '0', // Disable dictionary for technical terms
classify_enable_learning: '0', // Disable learning for consistent results
textord_really_old_xheight: '1' // Better handling of mixed text sizes
});
this.isInitialized = true;
console.error('FlexaBrain Enhanced OCR: Worker initialized successfully');
}
catch (error) {
console.error('FlexaBrain Enhanced OCR: Failed to initialize:', error);
throw new Error(`Enhanced OCR initialization failed: ${error instanceof Error ? error.message : String(error)}`);
}
}
/**
* Process a complete schematic page with enhanced OCR capabilities
*/
async processSchematicPage(page, options = {}) {
if (!this.isInitialized || !this.worker) {
throw new Error('Enhanced OCR service not initialized');
}
const startTime = Date.now();
console.error(`FlexaBrain Enhanced OCR: Processing page ${page.page_number}`);
try {
// Apply preprocessing filters
const preprocessedImage = await this.preprocessImage(page.image_data, options);
// Perform multi-resolution OCR for better accuracy
const ocrResult = await this.performMultiResolutionOCR(preprocessedImage, options);
// Enhance results with generator monitoring patterns
if (options.generator_monitoring_mode) {
ocrResult.words = this.enhanceGeneratorComponentRecognition(ocrResult.words);
}
// Extract technical text regions
const textRegions = await this.extractTechnicalTextRegions(ocrResult, page.dimensions);
// Detect lines and connections if enabled
let lineDetection;
if (options.enable_line_detection) {
lineDetection = await this.detectLinesAndConnections(preprocessedImage, ocrResult);
}
// Recognize electrical symbols if enabled
let symbolRecognition;
if (options.enable_symbol_recognition) {
symbolRecognition = await this.recognizeElectricalSymbols(preprocessedImage);
}
// Cache results for performance
const cacheKey = `${page.page_number}_${JSON.stringify(options)}`;
this.processingCache.set(cacheKey, ocrResult);
const totalTime = Date.now() - startTime;
console.error(`FlexaBrain Enhanced OCR: Page ${page.page_number} processed in ${totalTime}ms`);
const result = {
ocr_result: {
...ocrResult,
processing_time: totalTime
}
};
if (lineDetection) {
result.line_detection = lineDetection;
}
if (symbolRecognition) {
result.symbols = symbolRecognition;
}
return result;
}
catch (error) {
console.error(`Enhanced OCR processing failed for page ${page.page_number}:`, error);
throw new Error(`Enhanced OCR failed: ${error instanceof Error ? error.message : String(error)}`);
}
}
/**
* Preprocess image for better OCR results on technical drawings
*/
async preprocessImage(imageData, options) {
// Mock implementation - in production would use Sharp or similar
console.error('FlexaBrain Enhanced OCR: Applying image preprocessing filters');
const filters = options.preprocessing_filters || ['denoise', 'sharpen', 'contrast'];
// Simulate preprocessing operations
for (const filter of filters) {
switch (filter) {
case 'denoise':
console.error(' - Applying noise reduction');
break;
case 'sharpen':
console.error(' - Applying sharpening filter');
break;
case 'contrast':
console.error(' - Enhancing contrast');
break;
case 'binarize':
console.error(' - Converting to binary image');
break;
case 'deskew':
console.error(' - Correcting skew');
break;
}
}
return imageData; // Return original for mock - would return processed image
}
/**
* Perform OCR at multiple resolutions for better accuracy
*/
async performMultiResolutionOCR(imageData, options) {
if (!this.worker) {
throw new Error('OCR worker not available');
}
const resolutions = [options.dpi || 300];
if (options.dpi && options.dpi < 400) {
resolutions.push(options.dpi * 1.5); // Try higher resolution for small text
}
let bestResult = null;
let bestConfidence = 0;
for (const dpi of resolutions) {
try {
console.error(`FlexaBrain Enhanced OCR: Processing at ${dpi} DPI`);
// Configure OCR parameters for current resolution
await this.worker.setParameters({
tessedit_pageseg_mode: options.psm || PSM.SPARSE_TEXT,
// Adjust parameters based on DPI
textord_min_xheight: dpi < 200 ? 8 : 12,
textord_max_xheight: dpi < 200 ? 40 : 60
});
const result = await this.worker.recognize(imageData);
if (result.data.confidence > bestConfidence) {
bestConfidence = result.data.confidence;
bestResult = result;
}
}
catch (error) {
console.error(`OCR failed at ${dpi} DPI:`, error);
}
}
if (!bestResult) {
throw new Error('OCR failed at all resolutions');
}
// Extract words with enhanced bounding boxes
const words = bestResult.data.words
.filter((word) => word.text.trim().length > 0)
.map((word) => ({
text: word.text.trim(),
confidence: word.confidence / 100,
bbox: {
x: word.bbox.x0,
y: word.bbox.y0,
width: word.bbox.x1 - word.bbox.x0,
height: word.bbox.y1 - word.bbox.y0
}
}));
return {
text: bestResult.data.text.trim(),
confidence: bestResult.data.confidence / 100,
words,
processing_time: 0, // Will be set by caller
language: options.language || 'eng'
};
}
/**
* Enhance component recognition with generator monitoring patterns
*/
enhanceGeneratorComponentRecognition(words) {
return words.map(word => {
const upperText = word.text.toUpperCase();
let maxConfidenceBoost = 0;
let bestPattern = '';
// Check against generator monitoring patterns
for (const [patternName, pattern] of Object.entries(this.GENERATOR_COMPONENT_PATTERNS)) {
for (const regex of pattern.patterns) {
if (regex.test(upperText)) {
if (pattern.confidence_boost > maxConfidenceBoost) {
maxConfidenceBoost = pattern.confidence_boost;
bestPattern = patternName;
}
}
}
}
// Apply confidence boost if pattern matched
if (maxConfidenceBoost > 0) {
const enhancedConfidence = Math.min(word.confidence * maxConfidenceBoost, 1.0);
console.error(`FlexaBrain Enhanced OCR: Boosted confidence for ${word.text} (${bestPattern}): ${word.confidence.toFixed(3)} -> ${enhancedConfidence.toFixed(3)}`);
return {
...word,
confidence: enhancedConfidence
};
}
return word;
});
}
/**
* Extract technical text regions (title blocks, component lists, etc.)
*/
async extractTechnicalTextRegions(ocrResult, dimensions) {
const regions = [];
// Mock implementation - would analyze text positioning and content
const titleBlockRegion = {
region_type: 'title_block',
location: {
x: dimensions.width - 400,
y: dimensions.height - 200,
width: 380,
height: 180
},
text_content: 'SCHEMATIC TRACTION F6.00.32 Generator Monitor Control P01',
confidence: 0.9
};
regions.push(titleBlockRegion);
// Look for component lists based on text patterns
const componentListWords = ocrResult.words.filter(word => /^[A-Z]\d+[A-Z]?$/.test(word.text.toUpperCase()));
if (componentListWords.length > 5) {
// Likely has a component list
const minX = Math.min(...componentListWords.map(w => w.bbox.x));
const maxX = Math.max(...componentListWords.map(w => w.bbox.x + w.bbox.width));
const minY = Math.min(...componentListWords.map(w => w.bbox.y));
const maxY = Math.max(...componentListWords.map(w => w.bbox.y + w.bbox.height));
regions.push({
region_type: 'component_list',
location: { x: minX, y: minY, width: maxX - minX, height: maxY - minY },
text_content: componentListWords.map(w => w.text).join(' '),
confidence: 0.85
});
}
return regions;
}
/**
* Detect lines and electrical connections
*/
async detectLinesAndConnections(imageData, ocrResult) {
const startTime = Date.now();
// Mock implementation - would use computer vision for line detection
console.error('FlexaBrain Enhanced OCR: Detecting lines and connections');
const mockLines = [
{
id: 'line_001',
start_point: { x: 100, y: 150 },
end_point: { x: 300, y: 150 },
line_type: 'power',
confidence: 0.85,
thickness: 2,
style: 'solid'
},
{
id: 'line_002',
start_point: { x: 300, y: 150 },
end_point: { x: 500, y: 150 },
line_type: 'control',
confidence: 0.78,
thickness: 1,
style: 'dashed'
}
];
const mockConnections = [
{
line_id: 'line_001',
connected_components: ['A601', 'CB101'],
connection_type: 'bidirectional',
wire_number: 'W001'
}
];
return {
lines: mockLines,
connections: mockConnections,
processing_time: Date.now() - startTime
};
}
/**
* Recognize electrical symbols in the schematic
*/
async recognizeElectricalSymbols(imageData) {
const startTime = Date.now();
// Mock implementation - would use computer vision for symbol recognition
console.error('FlexaBrain Enhanced OCR: Recognizing electrical symbols');
const mockSymbols = [
{
id: 'symbol_001',
symbol_type: 'transformer',
location: { x: 200, y: 100, width: 50, height: 40 },
confidence: 0.88,
parameters: { 'turns_ratio': '1:10', 'rating': '1000kVA' }
},
{
id: 'symbol_002',
symbol_type: 'switch',
location: { x: 400, y: 200, width: 30, height: 20 },
confidence: 0.82,
parameters: { 'type': 'SPDT', 'rating': '600V' }
}
];
return {
symbols: mockSymbols,
processing_time: Date.now() - startTime
};
}
/**
* Extract specific generator monitoring components
*/
async extractGeneratorMonitoringComponents(ocrResult) {
const components = [];
for (const word of ocrResult.words) {
const upperText = word.text.toUpperCase();
for (const [patternName, pattern] of Object.entries(this.GENERATOR_COMPONENT_PATTERNS)) {
for (const regex of pattern.patterns) {
if (regex.test(upperText)) {
components.push({
id: word.text,
type: pattern.component_type,
pattern_matched: patternName,
location: word.bbox,
confidence: word.confidence * pattern.confidence_boost,
associated_parameters: pattern.associated_parameters,
typical_locations: pattern.typical_locations
});
break;
}
}
}
}
return components;
}
/**
* Batch process multiple pages efficiently
*/
async batchProcessPages(pages, options = {}) {
const results = new Map();
console.error(`FlexaBrain Enhanced OCR: Batch processing ${pages.length} pages`);
// Process pages in parallel for better performance
const promises = pages.map(async (page) => {
try {
const result = await this.processSchematicPage(page, options);
results.set(page.page_number, result);
}
catch (error) {
console.error(`Failed to process page ${page.page_number}:`, error);
results.set(page.page_number, { error: error instanceof Error ? error.message : String(error) });
}
});
await Promise.all(promises);
console.error(`FlexaBrain Enhanced OCR: Batch processing completed - ${results.size} pages processed`);
return results;
}
/**
* Get processing statistics and performance metrics
*/
getProcessingStats() {
let totalConfidence = 0;
let count = 0;
for (const result of this.processingCache.values()) {
totalConfidence += result.confidence;
count++;
}
return {
cache_size: this.processingCache.size,
total_processed: count,
average_confidence: count > 0 ? totalConfidence / count : 0,
worker_status: this.isInitialized && this.worker !== null
};
}
/**
* Clear processing cache to free memory
*/
clearCache() {
this.processingCache.clear();
console.error('FlexaBrain Enhanced OCR: Cache cleared');
}
/**
* Terminate the OCR service and clean up resources
*/
async terminate() {
if (this.worker) {
await this.worker.terminate();
this.worker = null;
this.isInitialized = false;
}
this.clearCache();
console.error('FlexaBrain Enhanced OCR: Service terminated');
}
}
// Export singleton instance
export const enhancedOCRService = new EnhancedOCRService();
// Graceful cleanup on process termination
process.on('exit', () => {
enhancedOCRService.terminate().catch(console.error);
});
process.on('SIGINT', () => {
enhancedOCRService.terminate().catch(console.error);
});
process.on('SIGTERM', () => {
enhancedOCRService.terminate().catch(console.error);
});
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